├── .github
    └── workflows
    │   └── run_tests.yml
├── .gitignore
├── .vscode
    └── settings.json
├── CHANGELOG.md
├── LICENSE
├── README.md
├── badges
    └── coverage.svg
├── create_docs.sh
├── docs
    ├── index.html
    └── timexseries
    │   ├── data_ingestion.html
    │   ├── data_prediction
    │       ├── index.html
    │       ├── models
    │       │   ├── arima_predictor.html
    │       │   ├── exponentialsmoothing_predictor.html
    │       │   ├── index.html
    │       │   ├── lstm_predictor.html
    │       │   ├── mockup_predictor.html
    │       │   ├── neuralprophet_predictor.html
    │       │   ├── predictor.html
    │       │   └── prophet_predictor.html
    │       ├── pipeline.html
    │       ├── transformation.html
    │       ├── validation_performances.html
    │       └── xcorr.html
    │   ├── data_visualization
    │       ├── functions.html
    │       └── index.html
    │   ├── images
    │       ├── Architecture.drawio
    │       └── Architecture.svg
    │   ├── index.html
    │   └── timeseries_container.html
├── examples
    ├── BitcoinPriceForecasting.ipynb
    ├── Covid-19SwitzerlandTicinoForecasting.ipynb
    ├── DailyMinTemperaturesForecasting.ipynb
    ├── datasets
    │   ├── BitcoinPrice.csv
    │   └── TicinoCovid-19.csv
    ├── figures
    │   ├── BitcoinPrice
    │   │   ├── Bitcoin_Data_Overview.png
    │   │   ├── Bitcoin_Historical_Plot.png
    │   │   ├── Bitcoin_Historical_Plot_Detail.png
    │   │   └── Bitcoin_Performance_Plot.png
    │   └── SwissCovid
    │   │   ├── Covid-19Ticino_data_overview.png
    │   │   ├── HistoricalPlotSwissCovid.png
    │   │   ├── TrainingWindows.drawio
    │   │   └── TrainingWindows.png
    └── historical_predictions
    │   ├── historical_predictions_bitcoin.pkl
    │   └── historical_predictions_swiss_covid_fbprophet.pkl
├── experiments
    ├── FreqEstimator
    │   ├── AEP.csv
    │   ├── AirlinePassengers.csv
    │   ├── BrentOil.csv
    │   ├── Covid.csv
    │   ├── FrequencyEstimator.ipynb
    │   ├── Steel.csv
    │   └── Test7.csv
    └── SingularMatrixError
    │   └── BitcoinPriceForecasting.ipynb
├── poetry.lock
├── pyproject.toml
├── tests
    ├── __init__.py
    ├── context.py
    ├── launch_tests.sh
    ├── test_data_ingestion.py
    ├── test_data_prediction.py
    ├── test_data_visualization.py
    ├── test_datasets
    │   ├── covid_example_data_ingestion.csv
    │   ├── data_visualization
    │   │   ├── create_demo.py
    │   │   ├── expected_children1.pkl
    │   │   └── test1.csv
    │   ├── infer_freq_datasets
    │   │   ├── <BusinessDay>_BrentOil.csv
    │   │   ├── <BusinessDay>_Copper.csv
    │   │   ├── <BusinessDay>_Gold.csv
    │   │   ├── <BusinessDay>_Steel.csv
    │   │   ├── <BusinessDay>_StockExample.csv
    │   │   ├── <Day>_Bitcoin.csv
    │   │   ├── <Day>_Covid.csv
    │   │   ├── <Hour>_AEP.csv
    │   │   ├── <MonthBegin>_APU.csv
    │   │   ├── <MonthBegin>_AirlinePassengers.csv
    │   │   └── <Week: weekday=4>_StockExample.csv
    │   ├── test_1.csv
    │   ├── test_1_1.csv
    │   ├── test_1_2.csv
    │   ├── test_2.csv
    │   ├── test_3.csv
    │   ├── test_4.csv
    │   ├── test_5.csv
    │   ├── test_5_1.csv
    │   ├── test_6.csv
    │   ├── test_6_expected.csv
    │   ├── test_7.csv
    │   ├── test_covid.csv
    │   ├── test_create_containers_extrareg_d.csv
    │   ├── test_create_containers_extrareg_e.csv
    │   └── test_data_reordering.csv
    └── utilities.py
└── timexseries
    ├── __init__.py
    ├── data_ingestion.py
    ├── data_prediction
        ├── __init__.py
        ├── models
        │   ├── __init__.py
        │   ├── arima.py
        │   ├── exponential_smoothing.py
        │   ├── flaml.py
        │   ├── linear.py
        │   ├── lstm.py
        │   ├── mockup.py
        │   ├── neuralprophet.py
        │   ├── persistence.py
        │   ├── predictor.py
        │   ├── prophet.py
        │   ├── random_walk_with_drift.py
        │   ├── seasonal_persistence.py
        │   └── seasonality_estimator.py
        ├── pipeline.py
        ├── transformation.py
        └── xcorr.py
    ├── data_visualization
        ├── __init__.py
        ├── functions.py
        └── locales
        │   ├── base.pot
        │   ├── en
        │       └── LC_MESSAGES
        │       │   ├── messages.mo
        │       │   └── messages.po
        │   └── it
        │       └── LC_MESSAGES
        │           ├── messages.mo
        │           └── messages.po
    └── documentation.md


/.github/workflows/run_tests.yml:
--------------------------------------------------------------------------------
 1 | name: Tests
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [ main ]
 6 | 
 7 | jobs:
 8 |   build:
 9 |     strategy:
10 |       fail-fast: false
11 |       matrix:
12 |         python-version: ['3.9', '3.10', '3.11']
13 |         poetry-version: ['1.2.2']
14 |         os: [ubuntu-latest]
15 | 
16 |     runs-on: ${{ matrix.os }}
17 | 
18 |     steps:
19 |     - uses: actions/checkout@v2
20 |     - name: Set up Python ${{ matrix.python-version }}
21 |       uses: actions/setup-python@v2
22 |       with:
23 |         python-version: ${{ matrix.python-version }}
24 | 
25 |     - name: Install poetry ${{ matrix.poetry-version }}
26 |       run: |
27 |         python -m ensurepip
28 |         python -m pip install --upgrade pip
29 |         python -m pip install poetry==${{ matrix.poetry-version }}
30 | 
31 |     - name: Install dependencies
32 |       shell: bash
33 |       run: python -m poetry install --with data_prediction,data_visualization
34 | 
35 |     - name: Test with pytest
36 |       run: |
37 |         cd tests
38 |         python -m poetry run python -m pytest -v
39 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | #*.mo
 56 | #*.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 | 
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | /.idea/
131 | /result_directory/
132 | 


--------------------------------------------------------------------------------
/.vscode/settings.json:
--------------------------------------------------------------------------------
1 | {
2 |     "python.testing.unittestEnabled": false,
3 |     "python.testing.pytestEnabled": true
4 | }


--------------------------------------------------------------------------------
/CHANGELOG.md:
--------------------------------------------------------------------------------
 1 | # Change Log
 2 | 
 3 | ## [1.2.2] - 2022-02-09
 4 | - Bump dependencies
 5 | - Managed different import mechanisms for Dash
 6 | 
 7 | ## [1.2.1] - 2021-06-23
 8 | - Removed automatic Seasonal part of ARIMA; it could take a long time. Further investigation on `pmdarima` is requested;
 9 | - Fixed a bug in which CSV with dates in the format `1959-01` were not correctly recognized;
10 | - Fixed a bug in Yeo-Johnson transformation which would "lose" the datetime index of transformed data;
11 | - Bump dependencies.
12 | 
13 | ## [1.2.0] - 2021-04-08
14 | 
15 | ### Added
16 | - Exponential Smoothing model, with automatic selection of seasonality and inner model
17 | - Options to set max and min values for predictions (if upper/lowerbounds are known beforehand)
18 | - Option to round the predictions to the nearest integer
19 | - Added `_all` key to set additional regressors for all the time-series in a dataset
20 | 
21 | ### Changed
22 | - Multithreading now uses Joblib: it should work on all platforms, and also for NeuralProphet/LSTM models
23 | - Fix bug: cross-correlation were wrong if max_lags was higher than the length of the dataset
24 | - Models can be specified in the `param_config` dictionary using lowercase names
25 | - Bump dependencies
26 | 
27 | ## [1.1.0] - 2021-03-08
28 | 
29 | ### Added
30 | 
31 | - Resizable histograms in data visualization
32 | - Historical error plot and histogram with percentual 
33 | - Buttons to quickly change box and aggregate box plots period
34 | - Added standard deviation of error in error metrics
35 | - New options for `model_parameters`: `min_values`/`max_values` which can be used to specify minimum and maximum
36 | values to use in prediction
37 | - New option for `model_parameters`: `round_to_integer` useful to point columns'names which should be predicted
38 | as integer numbers
39 | 
40 | ### Changed
41 | 
42 | - When using `max_threads: 1` multiprocessing is completely ignored, this temporarily fix some issues on MacOs. 
43 | Multiprocessing should be rewritten to work on all platforms.
44 | 
45 | ## [1.0.0] - 2021-02-17
46 | 
47 | Initial release.


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # TIMEX
 2 | [![Tests with PyTest](https://github.com/AlexMV12/TIMEX/actions/workflows/run_tests.yml/badge.svg)](https://github.com/AlexMV12/TIMEX/actions/workflows/run_tests.yml)
 3 | ![Coverage](badges/coverage.svg)
 4 | ![PyPI](https://img.shields.io/pypi/v/timexseries)
 5 | ![PyPI - Downloads](https://img.shields.io/pypi/dm/timexseries)
 6 | 
 7 | TIMEX (referred in code as `timexseries`) is a framework for time-series-forecasting-as-a-service.
 8 | 
 9 | Its main goal is to provide a simple and generic tool to build websites and, more in general,
10 | platforms, able to provide the forecasting of time-series in the "as-a-service" manner.
11 | 
12 | This means that users should interact with the service as less as possible.
13 | 
14 | An example of the capabilities of TIMEX can be found at [covid-timex.it](https://covid-timex.it)  
15 | That website is built using the [Dash](https://dash.plotly.com/), on which the visualization
16 | part of TIMEX is built. A deep explanation is available in the 
17 | [dedicated repository](https://github.com/AlexMV12/covid-timex.it).
18 | 
19 | ## Installation
20 | The main two dependencies of TIMEX are [Facebook Prophet](https://github.com/facebook/prophet)
21 | and [PyTorch](https://pytorch.org/). 
22 | If you prefer, you can install them beforehand, maybe because you want to choose the CUDA/CPU
23 | version of Torch.
24 | 
25 | However, installation is as simple as running:
26 | 
27 | `pip install timexseries`
28 | 
29 | ## Get started
30 | Please, refer to the Examples folder. You will find some Jupyter Notebook which illustrate
31 | the main characteristics of TIMEX. A Notebook explaining the covid-timex.it website is present,
32 | along with the source code of the site, [here](https://github.com/AlexMV12/covid-timex.it).
33 | 
34 | ## Documentation
35 | The full documentation is available at [here](https://alexmv12.github.io/TIMEX/timexseries/index.html).
36 | 
37 | ## Contacts
38 | If you have questions, suggestions or problems, feel free to open an Issue.
39 | You can contact us at:
40 | 
41 | - alessandro.falcetta@polimi.it
42 | - manuel.roveri@polimi.it
43 | 
44 | 


--------------------------------------------------------------------------------
/badges/coverage.svg:
--------------------------------------------------------------------------------
 1 | <?xml version="1.0" encoding="UTF-8"?>
 2 | <svg xmlns="http://www.w3.org/2000/svg" width="99" height="20">
 3 |     <linearGradient id="b" x2="0" y2="100%">
 4 |         <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
 5 |         <stop offset="1" stop-opacity=".1"/>
 6 |     </linearGradient>
 7 |     <mask id="a">
 8 |         <rect width="99" height="20" rx="3" fill="#fff"/>
 9 |     </mask>
10 |     <g mask="url(#a)">
11 |         <path fill="#555" d="M0 0h63v20H0z"/>
12 |         <path fill="#e05d44" d="M63 0h36v20H63z"/>
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14 |     </g>
15 |     <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
16 |         <text x="31.5" y="15" fill="#010101" fill-opacity=".3">coverage</text>
17 |         <text x="31.5" y="14">coverage</text>
18 |         <text x="80" y="15" fill="#010101" fill-opacity=".3">27%</text>
19 |         <text x="80" y="14">27%</text>
20 |     </g>
21 | </svg>
22 | 


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/create_docs.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | 
3 | pdoc --config latex_math=True --force --html --output-dir docs timexseries


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/docs/index.html:
--------------------------------------------------------------------------------
1 | <meta http-equiv="refresh" content="0; url=./timexseries/" />
2 | 


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/docs/timexseries/data_prediction/index.html:
--------------------------------------------------------------------------------
 1 | <!doctype html>
 2 | <html lang="en">
 3 | <head>
 4 | <meta charset="utf-8">
 5 | <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
 6 | <meta name="generator" content="pdoc 0.9.2" />
 7 | <title>timexseries.data_prediction API documentation</title>
 8 | <meta name="description" content="" />
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11 | <link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
12 | <style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
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14 | <style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
15 | <script async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML" integrity="sha256-kZafAc6mZvK3W3v1pHOcUix30OHQN6pU/NO2oFkqZVw=" crossorigin></script>
16 | <script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
17 | <script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
18 | </head>
19 | <body>
20 | <main>
21 | <article id="content">
22 | <header>
23 | <h1 class="title">Module <code>timexseries.data_prediction</code></h1>
24 | </header>
25 | <section id="section-intro">
26 | <details class="source">
27 | <summary>
28 | <span>Expand source code</span>
29 | </summary>
30 | <pre><code class="python">from timexseries.data_prediction.models.predictor import PredictionModel
31 | from .validation_performances import ValidationPerformance
32 | from .pipeline import create_timeseries_containers</code></pre>
33 | </details>
34 | </section>
35 | <section>
36 | <h2 class="section-title" id="header-submodules">Sub-modules</h2>
37 | <dl>
38 | <dt><code class="name"><a title="timexseries.data_prediction.models" href="models/index.html">timexseries.data_prediction.models</a></code></dt>
39 | <dd>
40 | <div class="desc"></div>
41 | </dd>
42 | <dt><code class="name"><a title="timexseries.data_prediction.pipeline" href="pipeline.html">timexseries.data_prediction.pipeline</a></code></dt>
43 | <dd>
44 | <div class="desc"></div>
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46 | <dt><code class="name"><a title="timexseries.data_prediction.transformation" href="transformation.html">timexseries.data_prediction.transformation</a></code></dt>
47 | <dd>
48 | <div class="desc"></div>
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50 | <dt><code class="name"><a title="timexseries.data_prediction.validation_performances" href="validation_performances.html">timexseries.data_prediction.validation_performances</a></code></dt>
51 | <dd>
52 | <div class="desc"></div>
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73 | <li><h3>Super-module</h3>
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75 | <li><code><a title="timexseries" href="../index.html">timexseries</a></code></li>
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77 | </li>
78 | <li><h3><a href="#header-submodules">Sub-modules</a></h3>
79 | <ul>
80 | <li><code><a title="timexseries.data_prediction.models" href="models/index.html">timexseries.data_prediction.models</a></code></li>
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82 | <li><code><a title="timexseries.data_prediction.transformation" href="transformation.html">timexseries.data_prediction.transformation</a></code></li>
83 | <li><code><a title="timexseries.data_prediction.validation_performances" href="validation_performances.html">timexseries.data_prediction.validation_performances</a></code></li>
84 | <li><code><a title="timexseries.data_prediction.xcorr" href="xcorr.html">timexseries.data_prediction.xcorr</a></code></li>
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 22 | <header>
 23 | <h1 class="title">Module <code>timexseries.data_prediction.models</code></h1>
 24 | </header>
 25 | <section id="section-intro">
 26 | </section>
 27 | <section>
 28 | <h2 class="section-title" id="header-submodules">Sub-modules</h2>
 29 | <dl>
 30 | <dt><code class="name"><a title="timexseries.data_prediction.models.arima_predictor" href="arima_predictor.html">timexseries.data_prediction.models.arima_predictor</a></code></dt>
 31 | <dd>
 32 | <div class="desc"></div>
 33 | </dd>
 34 | <dt><code class="name"><a title="timexseries.data_prediction.models.darts" href="darts.html">timexseries.data_prediction.models.darts</a></code></dt>
 35 | <dd>
 36 | <div class="desc"></div>
 37 | </dd>
 38 | <dt><code class="name"><a title="timexseries.data_prediction.models.exponentialsmoothing_predictor" href="exponentialsmoothing_predictor.html">timexseries.data_prediction.models.exponentialsmoothing_predictor</a></code></dt>
 39 | <dd>
 40 | <div class="desc"></div>
 41 | </dd>
 42 | <dt><code class="name"><a title="timexseries.data_prediction.models.lstm_predictor" href="lstm_predictor.html">timexseries.data_prediction.models.lstm_predictor</a></code></dt>
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 47 | <dd>
 48 | <div class="desc"></div>
 49 | </dd>
 50 | <dt><code class="name"><a title="timexseries.data_prediction.models.neuralprophet_predictor" href="neuralprophet_predictor.html">timexseries.data_prediction.models.neuralprophet_predictor</a></code></dt>
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 58 | <dt><code class="name"><a title="timexseries.data_prediction.models.prophet_predictor" href="prophet_predictor.html">timexseries.data_prediction.models.prophet_predictor</a></code></dt>
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 72 | <h1>Index</h1>
 73 | <div class="toc">
 74 | <ul></ul>
 75 | </div>
 76 | <ul id="index">
 77 | <li><h3>Super-module</h3>
 78 | <ul>
 79 | <li><code><a title="timexseries.data_prediction" href="../index.html">timexseries.data_prediction</a></code></li>
 80 | </ul>
 81 | </li>
 82 | <li><h3><a href="#header-submodules">Sub-modules</a></h3>
 83 | <ul>
 84 | <li><code><a title="timexseries.data_prediction.models.arima_predictor" href="arima_predictor.html">timexseries.data_prediction.models.arima_predictor</a></code></li>
 85 | <li><code><a title="timexseries.data_prediction.models.darts" href="darts.html">timexseries.data_prediction.models.darts</a></code></li>
 86 | <li><code><a title="timexseries.data_prediction.models.exponentialsmoothing_predictor" href="exponentialsmoothing_predictor.html">timexseries.data_prediction.models.exponentialsmoothing_predictor</a></code></li>
 87 | <li><code><a title="timexseries.data_prediction.models.lstm_predictor" href="lstm_predictor.html">timexseries.data_prediction.models.lstm_predictor</a></code></li>
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 90 | <li><code><a title="timexseries.data_prediction.models.predictor" href="predictor.html">timexseries.data_prediction.models.predictor</a></code></li>
 91 | <li><code><a title="timexseries.data_prediction.models.prophet_predictor" href="prophet_predictor.html">timexseries.data_prediction.models.prophet_predictor</a></code></li>
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20 | <main>
21 | <article id="content">
22 | <header>
23 | <h1 class="title">Module <code>timexseries.data_visualization</code></h1>
24 | </header>
25 | <section id="section-intro">
26 | <details class="source">
27 | <summary>
28 | <span>Expand source code</span>
29 | </summary>
30 | <pre><code class="python">from .functions import create_timeseries_dash_children</code></pre>
31 | </details>
32 | </section>
33 | <section>
34 | <h2 class="section-title" id="header-submodules">Sub-modules</h2>
35 | <dl>
36 | <dt><code class="name"><a title="timexseries.data_visualization.functions" href="functions.html">timexseries.data_visualization.functions</a></code></dt>
37 | <dd>
38 | <div class="desc"></div>
39 | </dd>
40 | </dl>
41 | </section>
42 | <section>
43 | </section>
44 | <section>
45 | </section>
46 | <section>
47 | </section>
48 | </article>
49 | <nav id="sidebar">
50 | <h1>Index</h1>
51 | <div class="toc">
52 | <ul></ul>
53 | </div>
54 | <ul id="index">
55 | <li><h3>Super-module</h3>
56 | <ul>
57 | <li><code><a title="timexseries" href="../index.html">timexseries</a></code></li>
58 | </ul>
59 | </li>
60 | <li><h3><a href="#header-submodules">Sub-modules</a></h3>
61 | <ul>
62 | <li><code><a title="timexseries.data_visualization.functions" href="functions.html">timexseries.data_visualization.functions</a></code></li>
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/experiments/FreqEstimator/AirlinePassengers.csv:
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  1 | Month,Passengers
  2 | 1980-01,112
  3 | 1980-02,118
  4 | 1980-03,132
  5 | 1980-04,129
  6 | 1980-05,121
  7 | 1980-06,135
  8 | 1980-07,148
  9 | 1980-08,148
 10 | 1980-09,136
 11 | 1980-10,119
 12 | 1980-11,104
 13 | 1980-12,118
 14 | 1981-01,115
 15 | 1981-02,126
 16 | 1981-03,141
 17 | 1981-04,135
 18 | 1981-05,125
 19 | 1981-06,149
 20 | 1981-07,170
 21 | 1981-08,170
 22 | 1981-09,158
 23 | 1981-10,133
 24 | 1981-11,114
 25 | 1981-12,140
 26 | 1982-01,145
 27 | 1982-02,150
 28 | 1982-03,178
 29 | 1982-04,163
 30 | 1982-05,172
 31 | 1982-06,178
 32 | 1982-07,199
 33 | 1982-08,199
 34 | 1982-09,184
 35 | 1982-10,162
 36 | 1982-11,146
 37 | 1982-12,166
 38 | 1983-01,171
 39 | 1983-02,180
 40 | 1983-03,193
 41 | 1983-04,181
 42 | 1983-05,183
 43 | 1983-06,218
 44 | 1983-07,230
 45 | 1983-08,242
 46 | 1983-09,209
 47 | 1983-10,191
 48 | 1983-11,172
 49 | 1983-12,194
 50 | 1984-01,196
 51 | 1984-02,196
 52 | 1984-03,236
 53 | 1984-04,235
 54 | 1984-05,229
 55 | 1984-06,243
 56 | 1984-07,264
 57 | 1984-08,272
 58 | 1984-09,237
 59 | 1984-10,211
 60 | 1984-11,180
 61 | 1984-12,201
 62 | 1985-01,204
 63 | 1985-02,188
 64 | 1985-03,235
 65 | 1985-04,227
 66 | 1985-05,234
 67 | 1985-06,264
 68 | 1985-07,302
 69 | 1985-08,293
 70 | 1985-09,259
 71 | 1985-10,229
 72 | 1985-11,203
 73 | 1985-12,229
 74 | 1986-01,242
 75 | 1986-02,233
 76 | 1986-03,267
 77 | 1986-04,269
 78 | 1986-05,270
 79 | 1986-06,315
 80 | 1986-07,364
 81 | 1986-08,347
 82 | 1986-09,312
 83 | 1986-10,274
 84 | 1986-11,237
 85 | 1986-12,278
 86 | 1987-01,284
 87 | 1987-02,277
 88 | 1987-03,317
 89 | 1987-04,313
 90 | 1987-05,318
 91 | 1987-06,374
 92 | 1987-07,413
 93 | 1987-08,405
 94 | 1987-09,355
 95 | 1987-10,306
 96 | 1987-11,271
 97 | 1987-12,306
 98 | 1988-01,315
 99 | 1988-02,301
100 | 1988-03,356
101 | 1988-04,348
102 | 1988-05,355
103 | 1988-06,422
104 | 1988-07,465
105 | 1988-08,467
106 | 1988-09,404
107 | 1988-10,347
108 | 1988-11,305
109 | 1988-12,336
110 | 1989-01,340
111 | 1989-02,318
112 | 1989-03,362
113 | 1989-04,348
114 | 1989-05,363
115 | 1989-06,435
116 | 1989-07,491
117 | 1989-08,505
118 | 1989-09,404
119 | 1989-10,359
120 | 1989-11,310
121 | 1989-12,337
122 | 1990-01,360
123 | 1990-02,342
124 | 1990-03,406
125 | 1990-04,396
126 | 1990-05,420
127 | 1990-06,472
128 | 1990-07,548
129 | 1990-08,559
130 | 1990-09,463
131 | 1990-10,407
132 | 1990-11,362
133 | 1990-12,405
134 | 


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/experiments/FreqEstimator/BrentOil.csv:
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  1 | Date,Close/Last
  2 | 04/11/2023,85.61
  3 | 04/10/2023,84.18
  4 | 04/06/2023,85.12
  5 | 04/05/2023,84.99
  6 | 04/04/2023,84.94
  7 | 04/03/2023,84.93
  8 | 03/31/2023,79.77
  9 | 03/30/2023,79.27
 10 | 03/29/2023,78.28
 11 | 03/28/2023,78.65
 12 | 03/27/2023,78.12
 13 | 03/24/2023,74.99
 14 | 03/23/2023,75.91
 15 | 03/22/2023,76.69
 16 | 03/21/2023,75.32
 17 | 03/20/2023,73.79
 18 | 03/17/2023,72.97
 19 | 03/16/2023,74.7
 20 | 03/15/2023,73.69
 21 | 03/14/2023,77.45
 22 | 03/13/2023,80.77
 23 | 03/10/2023,82.78
 24 | 03/09/2023,81.59
 25 | 03/08/2023,82.66
 26 | 03/07/2023,83.29
 27 | 03/06/2023,86.18
 28 | 03/03/2023,85.83
 29 | 03/02/2023,84.75
 30 | 03/01/2023,84.31
 31 | 02/28/2023,83.89
 32 | 02/27/2023,82.45
 33 | 02/24/2023,83.16
 34 | 02/23/2023,82.21
 35 | 02/22/2023,80.6
 36 | 02/21/2023,83.05
 37 | 02/17/2023,83.0
 38 | 02/16/2023,85.14
 39 | 02/15/2023,85.38
 40 | 02/14/2023,85.58
 41 | 02/13/2023,86.61
 42 | 02/10/2023,86.39
 43 | 02/09/2023,84.5
 44 | 02/08/2023,85.09
 45 | 02/07/2023,83.69
 46 | 02/06/2023,80.99
 47 | 02/03/2023,79.94
 48 | 02/02/2023,82.17
 49 | 02/01/2023,82.84
 50 | 01/31/2023,84.49
 51 | 01/30/2023,84.9
 52 | 01/27/2023,86.66
 53 | 01/26/2023,87.47
 54 | 01/25/2023,86.12
 55 | 01/24/2023,86.13
 56 | 01/23/2023,88.19
 57 | 01/20/2023,87.63
 58 | 01/19/2023,86.16
 59 | 01/18/2023,84.98
 60 | 01/17/2023,85.92
 61 | 01/13/2023,85.28
 62 | 01/12/2023,84.03
 63 | 01/11/2023,82.67
 64 | 01/10/2023,80.1
 65 | 01/09/2023,79.65
 66 | 01/06/2023,78.57
 67 | 01/05/2023,78.69
 68 | 01/04/2023,77.84
 69 | 01/03/2023,82.1
 70 | 12/30/2022,85.91
 71 | 12/29/2022,82.26
 72 | 12/28/2022,83.26
 73 | 12/27/2022,84.33
 74 | 12/23/2022,83.92
 75 | 12/22/2022,80.98
 76 | 12/21/2022,82.2
 77 | 12/20/2022,79.99
 78 | 12/19/2022,79.8
 79 | 12/16/2022,79.04
 80 | 12/15/2022,81.21
 81 | 12/14/2022,82.7
 82 | 12/13/2022,80.68
 83 | 12/12/2022,77.99
 84 | 12/09/2022,76.1
 85 | 12/08/2022,76.15
 86 | 12/07/2022,77.17
 87 | 12/06/2022,79.35
 88 | 12/05/2022,82.68
 89 | 12/02/2022,85.57
 90 | 12/01/2022,86.88
 91 | 11/30/2022,85.43
 92 | 11/29/2022,83.03
 93 | 11/28/2022,83.19
 94 | 11/25/2022,83.63
 95 | 11/23/2022,85.41
 96 | 11/22/2022,88.36
 97 | 11/21/2022,87.45
 98 | 11/18/2022,87.62
 99 | 11/17/2022,89.78
100 | 11/16/2022,92.86
101 | 11/15/2022,93.86
102 | 11/14/2022,93.14
103 | 11/11/2022,95.99
104 | 11/10/2022,93.67
105 | 11/09/2022,92.65
106 | 11/08/2022,95.36
107 | 11/07/2022,97.92
108 | 11/04/2022,98.57
109 | 11/03/2022,94.67
110 | 11/02/2022,96.16
111 | 11/01/2022,94.65
112 | 10/31/2022,94.83
113 | 10/28/2022,95.77
114 | 10/27/2022,96.96
115 | 10/26/2022,95.69
116 | 10/25/2022,93.52
117 | 10/24/2022,93.26
118 | 10/21/2022,93.5
119 | 10/20/2022,92.38
120 | 10/19/2022,92.41
121 | 10/18/2022,90.03
122 | 10/17/2022,91.62
123 | 10/14/2022,91.63
124 | 10/13/2022,94.57
125 | 10/12/2022,92.45
126 | 10/11/2022,94.29
127 | 10/10/2022,96.19
128 | 10/07/2022,97.92
129 | 10/06/2022,94.42
130 | 10/05/2022,93.37
131 | 10/04/2022,91.8
132 | 10/03/2022,88.86
133 | 09/30/2022,87.96
134 | 09/29/2022,88.49
135 | 09/28/2022,89.32
136 | 09/27/2022,86.27
137 | 09/26/2022,84.06
138 | 09/23/2022,86.15
139 | 09/22/2022,90.46
140 | 09/21/2022,89.83
141 | 09/20/2022,90.62
142 | 09/19/2022,92.0
143 | 09/16/2022,91.35
144 | 09/15/2022,90.84
145 | 09/14/2022,94.1
146 | 09/13/2022,93.17
147 | 09/12/2022,94.0
148 | 09/09/2022,92.84
149 | 09/08/2022,89.15
150 | 09/07/2022,88.0
151 | 09/06/2022,92.83
152 | 09/02/2022,93.02
153 | 09/01/2022,92.36
154 | 08/31/2022,96.49
155 | 08/30/2022,99.31
156 | 08/29/2022,105.09
157 | 08/26/2022,100.99
158 | 08/25/2022,99.34
159 | 08/24/2022,101.22
160 | 08/23/2022,100.22
161 | 08/22/2022,96.48
162 | 08/19/2022,96.72
163 | 08/18/2022,96.59
164 | 08/17/2022,93.65
165 | 08/16/2022,92.34
166 | 08/15/2022,95.1
167 | 08/12/2022,98.15
168 | 08/11/2022,99.6
169 | 08/10/2022,97.4
170 | 08/09/2022,96.31
171 | 08/08/2022,96.65
172 | 08/05/2022,94.92
173 | 08/04/2022,94.12
174 | 08/03/2022,96.78
175 | 08/02/2022,100.54
176 | 08/01/2022,100.03
177 | 07/29/2022,103.97
178 | 07/28/2022,107.14
179 | 07/27/2022,106.62
180 | 07/26/2022,104.4
181 | 07/25/2022,105.15
182 | 07/22/2022,103.2
183 | 07/21/2022,103.86
184 | 07/20/2022,106.92
185 | 07/19/2022,107.35
186 | 07/18/2022,106.27
187 | 07/15/2022,101.16
188 | 07/14/2022,99.1
189 | 07/13/2022,99.57
190 | 07/12/2022,99.49
191 | 07/11/2022,107.1
192 | 07/08/2022,107.02
193 | 07/07/2022,104.65
194 | 07/06/2022,100.69
195 | 07/05/2022,102.77
196 | 07/01/2022,111.63
197 | 06/30/2022,114.81
198 | 06/29/2022,116.26
199 | 06/28/2022,117.98
200 | 06/27/2022,115.09
201 | 06/24/2022,113.12
202 | 06/23/2022,110.05
203 | 06/22/2022,111.74
204 | 06/21/2022,114.65
205 | 06/17/2022,113.12
206 | 06/16/2022,119.81
207 | 06/15/2022,118.51
208 | 06/14/2022,121.17
209 | 06/13/2022,122.27
210 | 06/10/2022,122.01
211 | 06/09/2022,123.07
212 | 06/08/2022,123.58
213 | 06/07/2022,120.57
214 | 06/06/2022,119.51
215 | 06/03/2022,119.72
216 | 06/02/2022,117.61
217 | 06/01/2022,116.29
218 | 05/31/2022,122.84
219 | 05/27/2022,119.43
220 | 05/26/2022,117.4
221 | 05/25/2022,114.03
222 | 05/24/2022,113.56
223 | 05/23/2022,113.42
224 | 05/20/2022,112.55
225 | 05/19/2022,112.04
226 | 05/18/2022,109.11
227 | 05/17/2022,111.93
228 | 05/16/2022,114.24
229 | 05/13/2022,111.55
230 | 05/12/2022,107.45
231 | 05/11/2022,107.51
232 | 05/10/2022,102.46
233 | 05/09/2022,105.94
234 | 05/06/2022,112.39
235 | 05/05/2022,110.9
236 | 05/04/2022,110.14
237 | 05/03/2022,104.97
238 | 05/02/2022,107.58
239 | 04/29/2022,109.34
240 | 04/28/2022,107.59
241 | 04/27/2022,105.32
242 | 04/26/2022,104.99
243 | 04/25/2022,102.32
244 | 04/22/2022,106.65
245 | 04/21/2022,108.33
246 | 04/20/2022,106.8
247 | 04/19/2022,107.25
248 | 04/18/2022,113.16
249 | 04/14/2022,111.7
250 | 04/13/2022,108.78
251 | 04/12/2022,104.64
252 | 04/11/2022,98.48
253 | 04/08/2022,102.78
254 | 04/07/2022,100.58
255 | 04/06/2022,101.07
256 | 04/05/2022,106.64
257 | 04/04/2022,107.53
258 | 04/01/2022,104.39
259 | 03/31/2022,107.91
260 | 03/30/2022,113.45
261 | 03/29/2022,110.23
262 | 03/28/2022,112.48
263 | 03/25/2022,120.65
264 | 03/24/2022,119.03
265 | 03/23/2022,121.6
266 | 03/22/2022,115.48
267 | 03/21/2022,115.62
268 | 03/18/2022,107.93
269 | 03/17/2022,106.64
270 | 03/16/2022,98.02
271 | 03/15/2022,99.91
272 | 03/14/2022,106.9
273 | 03/11/2022,112.67
274 | 03/10/2022,109.33
275 | 03/09/2022,111.14
276 | 03/08/2022,127.98
277 | 03/07/2022,123.21
278 | 03/04/2022,118.11
279 | 03/03/2022,110.46
280 | 03/02/2022,112.93
281 | 03/01/2022,104.97
282 | 02/28/2022,100.99
283 | 02/25/2022,97.93
284 | 02/24/2022,99.08
285 | 02/23/2022,97.13
286 | 02/22/2022,96.84
287 | 02/18/2022,93.54
288 | 02/17/2022,92.97
289 | 02/16/2022,94.81
290 | 02/15/2022,93.28
291 | 02/14/2022,96.48
292 | 02/11/2022,94.44
293 | 02/10/2022,91.41
294 | 02/09/2022,91.55
295 | 02/08/2022,90.78
296 | 02/07/2022,92.69
297 | 02/04/2022,93.27
298 | 02/03/2022,91.11
299 | 02/02/2022,89.47
300 | 02/01/2022,89.16
301 | 01/31/2022,91.21
302 | 01/28/2022,90.03
303 | 01/27/2022,89.34
304 | 01/26/2022,89.96
305 | 01/25/2022,88.2
306 | 01/24/2022,86.27
307 | 01/21/2022,87.89
308 | 01/20/2022,88.38
309 | 01/19/2022,88.44
310 | 01/18/2022,87.51
311 | 01/14/2022,86.06
312 | 01/13/2022,84.47
313 | 01/12/2022,84.67
314 | 01/11/2022,83.72
315 | 01/10/2022,80.87
316 | 01/07/2022,81.75
317 | 01/06/2022,81.99
318 | 01/05/2022,80.8
319 | 01/04/2022,80.0
320 | 01/03/2022,78.98
321 | 12/31/2021,77.78
322 | 12/30/2021,79.32
323 | 12/29/2021,79.23
324 | 12/28/2021,78.94
325 | 12/27/2021,78.6
326 | 12/23/2021,76.85
327 | 12/22/2021,75.29
328 | 12/21/2021,73.98
329 | 12/20/2021,71.52
330 | 12/17/2021,73.52
331 | 12/16/2021,75.02
332 | 12/15/2021,73.88
333 | 12/14/2021,73.7
334 | 12/13/2021,74.39
335 | 12/10/2021,75.15
336 | 12/09/2021,74.42
337 | 12/08/2021,75.82
338 | 12/07/2021,75.44
339 | 12/06/2021,73.08
340 | 12/03/2021,69.88
341 | 12/02/2021,69.67
342 | 12/01/2021,68.87
343 | 11/30/2021,70.57
344 | 11/29/2021,73.44
345 | 11/26/2021,72.72
346 | 11/24/2021,82.25
347 | 11/23/2021,82.31
348 | 11/22/2021,79.7
349 | 11/19/2021,78.89
350 | 11/18/2021,81.24
351 | 11/17/2021,80.28
352 | 11/16/2021,82.43
353 | 11/15/2021,82.05
354 | 11/12/2021,82.17
355 | 11/11/2021,82.87
356 | 11/10/2021,82.64
357 | 11/09/2021,84.78
358 | 11/08/2021,83.43
359 | 11/05/2021,82.74
360 | 11/04/2021,80.54
361 | 11/03/2021,81.99
362 | 11/02/2021,84.72
363 | 11/01/2021,84.71
364 | 10/29/2021,84.38
365 | 10/28/2021,84.32
366 | 10/27/2021,84.58
367 | 10/26/2021,86.4
368 | 10/25/2021,85.99
369 | 10/22/2021,85.53
370 | 10/21/2021,84.61
371 | 10/20/2021,85.82
372 | 10/19/2021,85.08
373 | 10/18/2021,84.33
374 | 10/15/2021,84.86
375 | 10/14/2021,84.0
376 | 10/13/2021,83.18
377 | 10/12/2021,83.42
378 | 10/11/2021,83.65
379 | 10/08/2021,82.39
380 | 10/07/2021,81.95
381 | 10/06/2021,81.08
382 | 10/05/2021,82.56
383 | 10/04/2021,81.26
384 | 10/01/2021,79.28
385 | 09/30/2021,78.52
386 | 09/29/2021,78.64
387 | 09/28/2021,79.09
388 | 09/27/2021,79.53
389 | 09/24/2021,78.09
390 | 09/23/2021,77.25
391 | 09/22/2021,76.19
392 | 09/21/2021,74.36
393 | 09/20/2021,73.92
394 | 09/17/2021,75.34
395 | 09/16/2021,75.67
396 | 09/15/2021,75.46
397 | 09/14/2021,73.6
398 | 09/13/2021,73.51
399 | 09/10/2021,72.92
400 | 09/09/2021,71.45
401 | 09/08/2021,72.6
402 | 09/07/2021,71.69
403 | 09/03/2021,72.61
404 | 09/02/2021,73.03
405 | 09/01/2021,71.59
406 | 08/31/2021,72.99
407 | 08/30/2021,73.41
408 | 08/27/2021,72.7
409 | 08/26/2021,71.07
410 | 08/25/2021,72.25
411 | 08/24/2021,71.05
412 | 08/23/2021,68.75
413 | 08/20/2021,65.18
414 | 08/19/2021,66.45
415 | 08/18/2021,68.23
416 | 08/17/2021,69.03
417 | 08/16/2021,69.51
418 | 08/13/2021,70.59
419 | 08/12/2021,71.31
420 | 08/11/2021,71.44
421 | 08/10/2021,70.63
422 | 08/09/2021,69.04
423 | 08/06/2021,70.7
424 | 08/05/2021,71.29
425 | 08/04/2021,70.38
426 | 08/03/2021,72.41
427 | 08/02/2021,72.89
428 | 07/30/2021,76.33
429 | 07/29/2021,76.05
430 | 07/28/2021,74.74
431 | 07/27/2021,74.48
432 | 07/26/2021,74.5
433 | 07/23/2021,74.1
434 | 07/22/2021,73.79
435 | 07/21/2021,72.23
436 | 07/20/2021,69.35
437 | 07/19/2021,68.62
438 | 07/16/2021,73.59
439 | 07/15/2021,73.47
440 | 07/14/2021,74.76
441 | 07/13/2021,76.49
442 | 07/12/2021,75.16
443 | 07/09/2021,75.55
444 | 07/08/2021,74.12
445 | 07/07/2021,73.43
446 | 07/06/2021,74.53
447 | 07/02/2021,76.17
448 | 07/01/2021,75.84
449 | 06/30/2021,75.13
450 | 06/29/2021,74.76
451 | 06/28/2021,74.68
452 | 06/25/2021,76.18
453 | 06/24/2021,75.56
454 | 06/23/2021,75.19
455 | 06/22/2021,74.81
456 | 06/21/2021,74.9
457 | 06/18/2021,73.51
458 | 06/17/2021,73.08
459 | 06/16/2021,74.39
460 | 06/15/2021,73.99
461 | 06/14/2021,72.86
462 | 06/11/2021,72.69
463 | 06/10/2021,72.52
464 | 06/09/2021,72.03
465 | 06/08/2021,72.22
466 | 06/07/2021,71.49
467 | 06/04/2021,71.89
468 | 06/03/2021,71.31
469 | 06/02/2021,71.35
470 | 06/01/2021,70.25
471 | 05/28/2021,69.63
472 | 05/27/2021,69.46
473 | 05/26/2021,68.87
474 | 05/25/2021,68.65
475 | 05/24/2021,68.46
476 | 05/21/2021,66.44
477 | 05/20/2021,65.11
478 | 05/19/2021,66.66
479 | 05/18/2021,68.71
480 | 05/17/2021,69.46
481 | 05/14/2021,68.71
482 | 05/13/2021,67.05
483 | 05/12/2021,69.32
484 | 05/11/2021,68.55
485 | 05/10/2021,68.32
486 | 05/07/2021,68.28
487 | 05/06/2021,68.09
488 | 05/05/2021,68.96
489 | 05/04/2021,68.88
490 | 05/03/2021,67.56
491 | 04/30/2021,67.25
492 | 04/29/2021,68.56
493 | 04/28/2021,67.27
494 | 04/27/2021,66.42
495 | 04/26/2021,65.65
496 | 04/23/2021,66.11
497 | 04/22/2021,65.4
498 | 04/21/2021,65.32
499 | 04/20/2021,66.57
500 | 04/19/2021,67.05
501 | 04/16/2021,66.77
502 | 


--------------------------------------------------------------------------------
/experiments/FreqEstimator/Covid.csv:
--------------------------------------------------------------------------------
  1 | Data,Nuovi casi
  2 | 2020-02-25,93
  3 | 2020-02-26,78
  4 | 2020-02-27,250
  5 | 2020-02-28,238
  6 | 2020-02-29,240
  7 | 2020-03-01,566
  8 | 2020-03-02,342
  9 | 2020-03-03,466
 10 | 2020-03-04,587
 11 | 2020-03-05,769
 12 | 2020-03-06,778
 13 | 2020-03-07,1247
 14 | 2020-03-08,1492
 15 | 2020-03-09,1797
 16 | 2020-03-10,977
 17 | 2020-03-11,2313
 18 | 2020-03-12,2651
 19 | 2020-03-13,2547
 20 | 2020-03-14,3497
 21 | 2020-03-15,3590
 22 | 2020-03-16,3233
 23 | 2020-03-17,3526
 24 | 2020-03-18,4207
 25 | 2020-03-19,5322
 26 | 2020-03-20,5986
 27 | 2020-03-21,6557
 28 | 2020-03-22,5560
 29 | 2020-03-23,4789
 30 | 2020-03-24,5249
 31 | 2020-03-25,5210
 32 | 2020-03-26,6153
 33 | 2020-03-27,5959
 34 | 2020-03-28,5974
 35 | 2020-03-29,5217
 36 | 2020-03-30,4050
 37 | 2020-03-31,4053
 38 | 2020-04-01,4782
 39 | 2020-04-02,4668
 40 | 2020-04-03,4585
 41 | 2020-04-04,4805
 42 | 2020-04-05,4316
 43 | 2020-04-06,3599
 44 | 2020-04-07,3039
 45 | 2020-04-08,3836
 46 | 2020-04-09,4204
 47 | 2020-04-10,3951
 48 | 2020-04-11,4694
 49 | 2020-04-12,4092
 50 | 2020-04-13,3153
 51 | 2020-04-14,2972
 52 | 2020-04-15,2667
 53 | 2020-04-16,3786
 54 | 2020-04-17,3493
 55 | 2020-04-18,3491
 56 | 2020-04-19,3047
 57 | 2020-04-20,2256
 58 | 2020-04-21,2729
 59 | 2020-04-22,3370
 60 | 2020-04-23,2646
 61 | 2020-04-24,3021
 62 | 2020-04-25,2357
 63 | 2020-04-26,2324
 64 | 2020-04-27,1739
 65 | 2020-04-28,2091
 66 | 2020-04-29,2086
 67 | 2020-04-30,1872
 68 | 2020-05-01,1965
 69 | 2020-05-02,1900
 70 | 2020-05-03,1389
 71 | 2020-05-04,1221
 72 | 2020-05-05,1075
 73 | 2020-05-06,1444
 74 | 2020-05-07,1401
 75 | 2020-05-08,1327
 76 | 2020-05-09,1083
 77 | 2020-05-10,802
 78 | 2020-05-11,744
 79 | 2020-05-12,1402
 80 | 2020-05-13,888
 81 | 2020-05-14,992
 82 | 2020-05-15,789
 83 | 2020-05-16,875
 84 | 2020-05-17,675
 85 | 2020-05-18,451
 86 | 2020-05-19,813
 87 | 2020-05-20,665
 88 | 2020-05-21,642
 89 | 2020-05-22,652
 90 | 2020-05-23,669
 91 | 2020-05-24,531
 92 | 2020-05-25,300
 93 | 2020-05-26,397
 94 | 2020-05-27,584
 95 | 2020-05-28,593
 96 | 2020-05-29,516
 97 | 2020-05-30,416
 98 | 2020-05-31,355
 99 | 2020-06-01,178
100 | 2020-06-02,318
101 | 2020-06-03,321
102 | 2020-06-04,177
103 | 2020-06-05,518
104 | 2020-06-06,270
105 | 2020-06-07,197
106 | 2020-06-08,280
107 | 2020-06-09,283
108 | 2020-06-10,202
109 | 2020-06-11,379
110 | 2020-06-12,163
111 | 2020-06-13,346
112 | 2020-06-14,338
113 | 2020-06-15,303
114 | 2020-06-16,210
115 | 2020-06-17,329
116 | 2020-06-18,333
117 | 2020-06-19,251
118 | 2020-06-20,262
119 | 2020-06-21,224
120 | 2020-06-22,218
121 | 2020-06-23,122
122 | 2020-06-24,190
123 | 2020-06-25,296
124 | 2020-06-26,259
125 | 2020-06-27,175
126 | 2020-06-28,174
127 | 2020-06-29,126
128 | 2020-06-30,142
129 | 2020-07-01,187
130 | 2020-07-02,201
131 | 2020-07-03,223
132 | 2020-07-04,235
133 | 2020-07-05,192
134 | 2020-07-06,208
135 | 2020-07-07,138
136 | 2020-07-08,193
137 | 2020-07-09,229
138 | 2020-07-10,276
139 | 2020-07-11,188
140 | 2020-07-12,234
141 | 2020-07-13,169
142 | 2020-07-14,114
143 | 2020-07-15,163
144 | 2020-07-16,230
145 | 2020-07-17,233
146 | 2020-07-18,249
147 | 2020-07-19,219
148 | 2020-07-20,190
149 | 2020-07-21,129
150 | 2020-07-22,282
151 | 2020-07-23,306
152 | 2020-07-24,252
153 | 2020-07-25,275
154 | 2020-07-26,255
155 | 2020-07-27,170
156 | 2020-07-28,212
157 | 2020-07-29,289
158 | 2020-07-30,386
159 | 2020-07-31,379
160 | 2020-08-01,295
161 | 2020-08-02,239
162 | 2020-08-03,159
163 | 2020-08-04,190
164 | 2020-08-05,384
165 | 2020-08-06,402
166 | 2020-08-07,552
167 | 2020-08-08,347
168 | 2020-08-09,463
169 | 2020-08-10,259
170 | 2020-08-11,412
171 | 2020-08-12,481
172 | 2020-08-13,523
173 | 2020-08-14,574
174 | 2020-08-15,629
175 | 2020-08-16,479
176 | 2020-08-17,320
177 | 2020-08-18,403
178 | 2020-08-19,642
179 | 2020-08-20,845
180 | 2020-08-21,947
181 | 2020-08-22,1071
182 | 2020-08-23,1210
183 | 2020-08-24,953
184 | 2020-08-25,878
185 | 2020-08-26,1367
186 | 2020-08-27,1411
187 | 2020-08-28,1462
188 | 2020-08-29,1444
189 | 2020-08-30,1365
190 | 2020-08-31,996
191 | 2020-09-01,978
192 | 2020-09-02,1326
193 | 2020-09-03,1397
194 | 2020-09-04,1733
195 | 2020-09-05,1694
196 | 2020-09-06,1297
197 | 2020-09-07,1108
198 | 2020-09-08,1370
199 | 2020-09-09,1434
200 | 2020-09-10,1597
201 | 2020-09-11,1616
202 | 2020-09-12,1501
203 | 2020-09-13,1458
204 | 2020-09-14,1008
205 | 2020-09-15,1229
206 | 2020-09-16,1452
207 | 2020-09-17,1585
208 | 2020-09-18,1907
209 | 2020-09-19,1638
210 | 2020-09-20,1587
211 | 2020-09-21,1350
212 | 2020-09-22,1392
213 | 2020-09-23,1640
214 | 2020-09-24,1786
215 | 2020-09-25,1912
216 | 2020-09-26,1869
217 | 2020-09-27,1766
218 | 2020-09-28,1494
219 | 2020-09-29,1648
220 | 2020-09-30,1851
221 | 2020-10-01,2548
222 | 2020-10-02,2499
223 | 2020-10-03,2844
224 | 2020-10-04,2578
225 | 2020-10-05,2257
226 | 2020-10-06,2677
227 | 2020-10-07,3678
228 | 2020-10-08,4458
229 | 2020-10-09,5372
230 | 2020-10-10,5724
231 | 2020-10-11,5456
232 | 2020-10-12,4619
233 | 2020-10-13,5901
234 | 2020-10-14,7332
235 | 2020-10-15,8804
236 | 2020-10-16,10010
237 | 2020-10-17,10925
238 | 2020-10-18,11705
239 | 2020-10-19,9338
240 | 2020-10-20,10874
241 | 2020-10-21,15199
242 | 2020-10-22,16079
243 | 2020-10-23,19143
244 | 2020-10-24,19644
245 | 2020-10-25,21273
246 | 2020-10-26,17012
247 | 2020-10-27,21994
248 | 2020-10-28,24991
249 | 2020-10-29,26831
250 | 2020-10-30,31084
251 | 2020-10-31,31758
252 | 2020-11-01,29907
253 | 2020-11-02,22253
254 | 2020-11-03,28244
255 | 2020-11-04,30550
256 | 2020-11-05,34505
257 | 2020-11-06,37809
258 | 2020-11-07,39811
259 | 2020-11-08,32616
260 | 2020-11-09,25271
261 | 2020-11-10,35098
262 | 2020-11-11,32961
263 | 2020-11-12,37978
264 | 2020-11-13,40902
265 | 2020-11-14,37255
266 | 2020-11-15,33979
267 | 2020-11-16,27354
268 | 2020-11-17,32191
269 | 2020-11-18,34282
270 | 2020-11-19,36176
271 | 2020-11-20,37242
272 | 2020-11-21,34767
273 | 2020-11-22,28337
274 | 2020-11-23,22930
275 | 2020-11-24,23232
276 | 2020-11-25,25853
277 | 2020-11-26,29003
278 | 2020-11-27,28352
279 | 2020-11-28,26323
280 | 2020-11-29,20648
281 | 2020-11-30,16377
282 | 2020-12-01,19350
283 | 2020-12-02,20709
284 | 2020-12-03,23225
285 | 2020-12-04,24099
286 | 2020-12-05,21052
287 | 2020-12-06,18887
288 | 2020-12-07,13720
289 | 2020-12-08,14842
290 | 2020-12-09,12756
291 | 2020-12-10,16999
292 | 2020-12-11,18727
293 | 2020-12-12,19903
294 | 2020-12-13,17938
295 | 2020-12-14,12030
296 | 2020-12-15,14844
297 | 2020-12-16,17572
298 | 2020-12-17,18236
299 | 2020-12-18,17992
300 | 2020-12-19,16308
301 | 2020-12-20,15104
302 | 2020-12-21,10872
303 | 2020-12-22,13318
304 | 2020-12-23,14522
305 | 2020-12-24,18040
306 | 2020-12-25,19037
307 | 2020-12-26,10431
308 | 2020-12-27,8913
309 | 2020-12-28,8585
310 | 2020-12-29,11224
311 | 2020-12-30,16202
312 | 2020-12-31,23477
313 | 2021-01-01,22211
314 | 2021-01-02,11831
315 | 2021-01-03,14245
316 | 2021-01-04,10800
317 | 2021-01-05,15378
318 | 2021-01-06,20331
319 | 2021-01-07,18020
320 | 2021-01-08,17533
321 | 2021-01-09,19978
322 | 2021-01-10,18627
323 | 2021-01-11,12532
324 | 2021-01-12,14242
325 | 2021-01-13,15774
326 | 2021-01-14,17246
327 | 2021-01-15,16146
328 | 2021-01-16,16310
329 | 2021-01-17,12545
330 | 2021-01-18,8825
331 | 2021-01-19,10497
332 | 2021-01-20,13571
333 | 2021-01-21,14078
334 | 2021-01-22,13633
335 | 2021-01-23,13331
336 | 2021-01-24,11629
337 | 2021-01-25,8562
338 | 2021-01-26,10593
339 | 2021-01-27,15204
340 | 2021-01-28,14372
341 | 2021-01-29,13574
342 | 2021-01-30,12715
343 | 2021-01-31,11252
344 | 2021-02-01,7925
345 | 2021-02-02,9660
346 | 2021-02-03,13189
347 | 2021-02-04,13659
348 | 2021-02-05,14218
349 | 2021-02-06,13442
350 | 2021-02-07,11641
351 | 2021-02-08,7970
352 | 2021-02-09,10630
353 | 2021-02-10,12956
354 | 2021-02-11,15146
355 | 2021-02-12,13908
356 | 2021-02-13,13532
357 | 2021-02-14,11068
358 | 2021-02-15,7351
359 | 2021-02-16,10386
360 | 2021-02-17,12074
361 | 2021-02-18,13762
362 | 2021-02-19,15479
363 | 2021-02-20,14931
364 | 2021-02-21,13452
365 | 2021-02-22,9630
366 | 2021-02-23,13314
367 | 2021-02-24,16424
368 | 2021-02-25,19886
369 | 2021-02-26,20499
370 | 2021-02-27,18916
371 | 2021-02-28,17455
372 | 2021-03-01,13114
373 | 2021-03-02,17083
374 | 2021-03-03,20884
375 | 2021-03-04,22865
376 | 2021-03-05,24036
377 | 2021-03-06,23641
378 | 2021-03-07,20765
379 | 2021-03-08,13902
380 | 2021-03-09,19749
381 | 2021-03-10,22409
382 | 2021-03-11,25673
383 | 2021-03-12,26824
384 | 2021-03-13,26062
385 | 2021-03-14,21315
386 | 2021-03-15,15267
387 | 2021-03-16,20396
388 | 2021-03-17,23059
389 | 2021-03-18,24935
390 | 2021-03-19,25735
391 | 2021-03-20,23832
392 | 2021-03-21,20159
393 | 2021-03-22,13846
394 | 2021-03-23,18765
395 | 2021-03-24,21267
396 | 2021-03-25,23798
397 | 2021-03-26,23987
398 | 2021-03-27,23776
399 | 2021-03-28,19611
400 | 2021-03-29,12916
401 | 2021-03-30,16013
402 | 2021-03-31,22516
403 | 2021-04-01,23649
404 | 2021-04-02,21932
405 | 2021-04-03,21261
406 | 2021-04-04,18025
407 | 2021-04-05,10680
408 | 2021-04-06,7767
409 | 2021-04-07,13708
410 | 2021-04-08,17221
411 | 2021-04-09,18938
412 | 2021-04-10,17567
413 | 2021-04-11,15746
414 | 2021-04-12,9789
415 | 2021-04-13,13447
416 | 2021-04-14,16168
417 | 2021-04-15,16974
418 | 2021-04-16,15943
419 | 2021-04-17,15370
420 | 2021-04-18,12694
421 | 2021-04-19,8864
422 | 2021-04-20,12074
423 | 2021-04-21,13844
424 | 2021-04-22,16050
425 | 2021-04-23,14761
426 | 2021-04-24,13817
427 | 2021-04-25,13158
428 | 2021-04-26,8444
429 | 2021-04-27,10404
430 | 2021-04-28,13385
431 | 2021-04-29,14320
432 | 2021-04-30,13446
433 | 2021-05-01,12965
434 | 2021-05-02,9148
435 | 2021-05-03,5948
436 | 2021-05-04,9116
437 | 2021-05-05,10585
438 | 2021-05-06,11807
439 | 2021-05-07,10554
440 | 2021-05-08,10176
441 | 2021-05-09,8292
442 | 2021-05-10,5080
443 | 2021-05-11,6946
444 | 2021-05-12,7852
445 | 2021-05-13,8085
446 | 2021-05-14,7567
447 | 2021-05-15,6659
448 | 2021-05-16,5753
449 | 2021-05-17,3455
450 | 2021-05-18,4452
451 | 2021-05-19,5506
452 | 2021-05-20,5741
453 | 2021-05-21,5218
454 | 2021-05-22,4717
455 | 2021-05-23,3995
456 | 2021-05-24,2490
457 | 2021-05-25,3224
458 | 2021-05-26,3937
459 | 2021-05-27,4147
460 | 2021-05-28,3738
461 | 2021-05-29,3351
462 | 2021-05-30,2949
463 | 2021-05-31,1820
464 | 2021-06-01,2483
465 | 2021-06-02,2897
466 | 2021-06-03,1968
467 | 2021-06-04,2557
468 | 2021-06-05,2436
469 | 2021-06-06,2275
470 | 2021-06-07,1273
471 | 2021-06-08,1896
472 | 2021-06-09,2199
473 | 2021-06-10,2079
474 | 2021-06-11,1901
475 | 2021-06-12,1723
476 | 2021-06-13,1390
477 | 2021-06-14,907
478 | 2021-06-15,1255
479 | 2021-06-16,1400
480 | 2021-06-17,1325
481 | 2021-06-18,1147
482 | 2021-06-19,1197
483 | 2021-06-20,881
484 | 2021-06-21,495
485 | 2021-06-22,835
486 | 2021-06-23,951
487 | 2021-06-24,927
488 | 2021-06-25,753
489 | 2021-06-26,838
490 | 2021-06-27,782
491 | 2021-06-28,389
492 | 2021-06-29,679
493 | 2021-06-30,776
494 | 2021-07-01,882
495 | 2021-07-02,794
496 | 2021-07-03,932
497 | 2021-07-04,808
498 | 2021-07-05,480
499 | 2021-07-06,907
500 | 2021-07-07,1010
501 | 2021-07-08,1394
502 | 


--------------------------------------------------------------------------------
/experiments/FreqEstimator/Steel.csv:
--------------------------------------------------------------------------------
  1 | Date,Close
  2 | 03/27/23,1818.82
  3 | 03/24/23,1798.15
  4 | 03/23/23,1793.7
  5 | 03/22/23,1792.86
  6 | 03/21/23,1823.13
  7 | 03/20/23,1793.32
  8 | 03/17/23,1757.5
  9 | 03/16/23,1791.6
 10 | 03/15/23,1766.93
 11 | 03/14/23,1889.84
 12 | 03/13/23,1874.8
 13 | 03/10/23,1900.49
 14 | 03/09/23,1967.73
 15 | 03/08/23,2036.38
 16 | 03/07/23,2010.15
 17 | 03/06/23,2051.34
 18 | 03/03/23,2103.99
 19 | 03/02/23,2071.19
 20 | 03/01/23,2041.47
 21 | 02/28/23,1980.48
 22 | 02/27/23,1959.16
 23 | 02/24/23,1934.49
 24 | 02/23/23,1971.61
 25 | 02/22/23,1976.52
 26 | 02/21/23,1989.49
 27 | 02/17/23,2015.7
 28 | 02/16/23,2033.64
 29 | 02/15/23,2018.28
 30 | 02/14/23,2001.49
 31 | 02/13/23,1972.33
 32 | 02/10/23,1949.78
 33 | 02/09/23,1948.47
 34 | 02/08/23,1979.4
 35 | 02/07/23,1990.08
 36 | 02/06/23,1994.34
 37 | 02/03/23,2034.61
 38 | 02/02/23,2035.71
 39 | 02/01/23,2060.79
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 41 | 01/30/23,2014.34
 42 | 01/27/23,2032.84
 43 | 01/26/23,2058.22
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 52 | 01/12/23,1949.28
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 60 | 12/30/22,1731.09
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 72 | 12/13/22,1765.72
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 86 | 11/22/22,1730.4
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 90 | 11/16/22,1699.31
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 92 | 11/14/22,1716.12
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 94 | 11/10/22,1630.76
 95 | 11/09/22,1583.59
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 98 | 11/04/22,1637.04
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100 | 11/02/22,1491.44
101 | 11/01/22,1566.0
102 | 10/31/22,1527.6
103 | 10/28/22,1515.46
104 | 10/27/22,1549.92
105 | 10/26/22,1569.01
106 | 10/25/22,1549.37
107 | 10/24/22,1545.64
108 | 10/21/22,1580.45
109 | 10/20/22,1500.53
110 | 10/19/22,1478.07
111 | 10/18/22,1498.03
112 | 10/17/22,1476.27
113 | 10/14/22,1437.51
114 | 10/13/22,1501.59
115 | 10/12/22,1464.68
116 | 10/11/22,1474.77
117 | 10/10/22,1484.97
118 | 10/07/22,1468.95
119 | 10/06/22,1486.24
120 | 10/05/22,1509.75
121 | 10/04/22,1518.65
122 | 10/03/22,1458.37
123 | 09/30/22,1372.03
124 | 09/29/22,1371.59
125 | 09/28/22,1395.9
126 | 09/27/22,1368.29
127 | 09/26/22,1343.26
128 | 09/23/22,1375.74
129 | 09/22/22,1439.03
130 | 09/21/22,1430.52
131 | 09/20/22,1458.59
132 | 09/19/22,1490.95
133 | 09/16/22,1449.24
134 | 09/15/22,1457.23
135 | 09/14/22,1466.5
136 | 09/13/22,1531.75
137 | 09/12/22,1598.49
138 | 09/09/22,1595.5
139 | 09/08/22,1519.28
140 | 09/07/22,1506.22
141 | 09/06/22,1493.14
142 | 09/02/22,1486.74
143 | 09/01/22,1479.72
144 | 08/31/22,1524.06
145 | 08/30/22,1548.73
146 | 08/29/22,1610.32
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148 | 08/25/22,1646.4
149 | 08/24/22,1597.03
150 | 08/23/22,1605.58
151 | 08/22/22,1550.85
152 | 08/19/22,1578.16
153 | 08/18/22,1619.96
154 | 08/17/22,1611.47
155 | 08/16/22,1646.71
156 | 08/15/22,1628.37
157 | 08/12/22,1653.55
158 | 08/11/22,1634.84
159 | 08/10/22,1631.17
160 | 08/09/22,1595.65
161 | 08/08/22,1573.62
162 | 08/05/22,1563.23
163 | 08/04/22,1533.14
164 | 08/03/22,1515.84
165 | 08/02/22,1532.48
166 | 08/01/22,1553.97
167 | 07/29/22,1569.48
168 | 07/28/22,1531.41
169 | 07/27/22,1510.9
170 | 07/26/22,1472.54
171 | 07/25/22,1476.71
172 | 07/22/22,1429.29
173 | 07/21/22,1463.31
174 | 07/20/22,1442.19
175 | 07/19/22,1445.08
176 | 07/18/22,1404.96
177 | 07/15/22,1384.88
178 | 07/14/22,1358.11
179 | 07/13/22,1417.9
180 | 07/12/22,1397.61
181 | 07/11/22,1398.97
182 | 07/08/22,1427.43
183 | 07/07/22,1437.24
184 | 07/06/22,1377.45
185 | 07/05/22,1384.2
186 | 07/01/22,1431.8
187 | 06/30/22,1441.03
188 | 06/29/22,1476.35
189 | 06/28/22,1497.29
190 | 06/27/22,1509.2
191 | 06/24/22,1494.77
192 | 06/23/22,1441.67
193 | 06/22/22,1475.37
194 | 06/21/22,1535.44
195 | 06/17/22,1533.2
196 | 06/16/22,1549.93
197 | 06/15/22,1623.49
198 | 06/14/22,1593.35
199 | 06/13/22,1606.41
200 | 06/10/22,1699.43
201 | 06/09/22,1741.4
202 | 06/08/22,1823.37
203 | 06/07/22,1900.84
204 | 06/06/22,1865.13
205 | 06/03/22,1848.83
206 | 06/02/22,1874.52
207 | 06/01/22,1830.34
208 | 05/31/22,1832.35
209 | 05/27/22,1873.94
210 | 05/26/22,1831.49
211 | 05/25/22,1783.33
212 | 05/24/22,1759.74
213 | 05/23/22,1763.91
214 | 05/20/22,1705.65
215 | 05/19/22,1687.92
216 | 05/18/22,1663.58
217 | 05/17/22,1727.7
218 | 05/16/22,1669.36
219 | 05/13/22,1652.33
220 | 05/12/22,1621.45
221 | 05/11/22,1639.16
222 | 05/10/22,1645.32
223 | 05/09/22,1658.19
224 | 05/06/22,1730.5
225 | 05/05/22,1757.13
226 | 05/04/22,1870.07
227 | 05/03/22,1836.86
228 | 05/02/22,1799.72
229 | 04/29/22,1824.25
230 | 04/28/22,1866.96
231 | 04/27/22,1828.06
232 | 04/26/22,1779.35
233 | 04/25/22,1837.95
234 | 04/22/22,1892.51
235 | 04/21/22,1968.22
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239 | 04/14/22,2039.58
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251 | 03/29/22,1988.15
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255 | 03/23/22,1973.42
256 | 03/22/22,1949.82
257 | 03/21/22,1961.14
258 | 03/18/22,1894.7
259 | 03/17/22,1881.01
260 | 03/16/22,1822.1
261 | 03/15/22,1774.47
262 | 03/14/22,1782.36
263 | 03/11/22,1842.49
264 | 03/10/22,1868.94
265 | 03/09/22,1830.73
266 | 03/08/22,1815.89
267 | 03/07/22,1867.29
268 | 03/04/22,1896.92
269 | 03/03/22,1907.94
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280 | 02/15/22,1720.67
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293 | 01/27/22,1548.37
294 | 01/26/22,1547.79
295 | 01/25/22,1551.11
296 | 01/24/22,1554.23
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310 | 01/03/22,1579.49
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312 | 12/30/21,1572.25
313 | 12/29/21,1567.14
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315 | 12/27/21,1581.27
316 | 12/23/21,1559.84
317 | 12/22/21,1561.22
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319 | 12/20/21,1510.98
320 | 12/17/21,1564.52
321 | 12/16/21,1566.54
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324 | 12/13/21,1529.98
325 | 12/10/21,1531.27
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330 | 12/03/21,1472.29
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349 | 11/05/21,1539.54
350 | 11/04/21,1535.93
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366 | 10/13/21,1582.0
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370 | 10/07/21,1549.27
371 | 10/06/21,1520.67
372 | 10/05/21,1544.69
373 | 10/04/21,1533.63
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375 | 09/30/21,1535.67
376 | 09/29/21,1533.5
377 | 09/28/21,1542.96
378 | 09/27/21,1580.91
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383 | 09/20/21,1542.09
384 | 09/17/21,1615.36
385 | 09/16/21,1681.81
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427 | 07/19/21,1672.98
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429 | 07/15/21,1778.0
430 | 07/14/21,1773.85
431 | 07/13/21,1774.22
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433 | 07/09/21,1784.16
434 | 07/08/21,1713.87
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436 | 07/06/21,1719.48
437 | 07/02/21,1771.06
438 | 07/01/21,1758.47
439 | 06/30/21,1766.28
440 | 06/29/21,1762.01
441 | 06/28/21,1750.09
442 | 06/25/21,1770.74
443 | 06/24/21,1780.84
444 | 06/23/21,1743.59
445 | 06/22/21,1730.56
446 | 06/21/21,1722.57
447 | 06/18/21,1678.96
448 | 06/17/21,1699.31
449 | 06/16/21,1766.03
450 | 06/15/21,1812.56
451 | 06/14/21,1827.9
452 | 06/11/21,1866.4
453 | 06/10/21,1839.43
454 | 06/09/21,1840.53
455 | 06/08/21,1838.4
456 | 06/07/21,1825.75
457 | 06/04/21,1855.1
458 | 06/03/21,1840.22
459 | 06/02/21,1859.37
460 | 06/01/21,1874.75
461 | 05/28/21,1809.11
462 | 05/27/21,1808.26
463 | 05/26/21,1759.48
464 | 05/25/21,1735.37
465 | 05/24/21,1777.4
466 | 05/21/21,1765.29
467 | 05/20/21,1785.83
468 | 05/19/21,1792.67
469 | 05/18/21,1853.25
470 | 05/17/21,1862.97
471 | 05/14/21,1825.25
472 | 05/13/21,1833.25
473 | 05/12/21,1835.93
474 | 05/11/21,1927.01
475 | 05/10/21,1895.48
476 | 05/07/21,1893.74
477 | 05/06/21,1854.66
478 | 05/05/21,1803.53
479 | 05/04/21,1772.92
480 | 05/03/21,1737.32
481 | 04/30/21,1701.78
482 | 04/29/21,1744.86
483 | 04/28/21,1753.49
484 | 04/27/21,1734.21
485 | 04/26/21,1738.36
486 | 04/23/21,1687.6
487 | 04/22/21,1647.17
488 | 04/21/21,1658.98
489 | 04/20/21,1628.09
490 | 04/19/21,1665.74
491 | 04/16/21,1667.62
492 | 04/15/21,1648.48
493 | 04/14/21,1639.6
494 | 04/13/21,1590.48
495 | 04/12/21,1591.39
496 | 04/09/21,1603.14
497 | 04/08/21,1619.6
498 | 04/07/21,1621.07
499 | 04/06/21,1615.61
500 | 04/05/21,1630.82
501 | 04/01/21,1593.3
502 | 


--------------------------------------------------------------------------------
/experiments/FreqEstimator/Test7.csv:
--------------------------------------------------------------------------------
  1 | Data,Ultimo
  2 | 2021-01-08,1.955
  3 | 2021-01-15,1.902
  4 | 2021-01-22,1.8842
  5 | 2021-01-29,1.8052
  6 | 2021-02-05,2.061
  7 | 2021-02-12,2.096
  8 | 2021-02-19,2.1085
  9 | 2021-02-26,2.134
 10 | 2021-03-05,2.1945
 11 | 2021-03-12,2.2805
 12 | 2021-03-19,2.314
 13 | 2021-03-26,2.2975
 14 | 2021-04-09,2.2535
 15 | 2021-04-16,2.2955
 16 | 2021-04-23,2.221
 17 | 2021-04-30,2.321
 18 | 2021-05-07,2.362
 19 | 2021-05-14,2.3875
 20 | 2021-05-21,2.374
 21 | 2021-05-28,2.409
 22 | 2021-06-04,2.4805
 23 | 2021-06-11,2.425
 24 | 2021-06-18,2.344
 25 | 2021-06-25,2.3855
 26 | 2021-07-02,2.336
 27 | 2021-07-09,2.284
 28 | 2021-07-16,2.232
 29 | 2021-07-23,2.285
 30 | 2021-07-30,2.3315
 31 | 2021-08-06,2.3985
 32 | 2021-08-13,2.448
 33 | 2021-08-20,2.369
 34 | 2021-08-27,2.378
 35 | 2021-09-03,2.3875
 36 | 2021-09-10,2.3465
 37 | 2021-09-17,2.374
 38 | 2021-09-24,2.4245
 39 | 2021-10-01,2.449
 40 | 2021-10-08,2.5175
 41 | 2021-10-15,2.575
 42 | 2021-10-22,2.4515
 43 | 2021-10-29,2.4575
 44 | 2021-11-05,2.4835
 45 | 2021-11-12,2.4715
 46 | 2021-11-19,2.384
 47 | 2021-11-26,2.1565
 48 | 2021-12-03,2.1465
 49 | 2021-12-10,2.2155
 50 | 2021-12-17,2.2055
 51 | 2022-01-07,2.4515
 52 | 2022-01-14,2.562
 53 | 2022-01-21,2.552
 54 | 2022-01-28,2.597
 55 | 2022-02-04,2.667
 56 | 2022-02-11,2.8535
 57 | 2022-02-18,2.722
 58 | 2022-02-25,2.4775
 59 | 2022-03-04,1.8962
 60 | 2022-03-11,1.9194
 61 | 2022-03-18,2.086
 62 | 2022-03-25,2.033
 63 | 2022-04-01,2.105
 64 | 2022-04-08,1.982
 65 | 2022-04-22,1.9828
 66 | 2022-04-29,1.9548
 67 | 2022-05-06,1.88
 68 | 2022-05-13,1.9724
 69 | 2022-05-20,1.983
 70 | 2022-05-27,2.047
 71 | 2022-06-03,1.973
 72 | 2022-06-10,1.8216
 73 | 2022-06-17,1.8036
 74 | 2022-06-24,1.8822
 75 | 2022-07-01,1.7676
 76 | 2022-07-08,1.7626
 77 | 2022-07-15,1.632
 78 | 2022-07-22,1.66
 79 | 2022-07-29,1.7286
 80 | 2022-08-05,1.814
 81 | 2022-08-12,1.8538
 82 | 2022-08-19,1.7732
 83 | 2022-08-26,1.685
 84 | 2022-09-02,1.7382
 85 | 2022-09-09,1.8332
 86 | 2022-09-16,1.915
 87 | 2022-09-23,1.764
 88 | 2022-09-30,1.7028
 89 | 2022-10-07,1.7056
 90 | 2022-10-14,1.7124
 91 | 2022-10-21,1.8276
 92 | 2022-10-28,1.9052
 93 | 2022-11-04,2.065
 94 | 2022-11-11,2.1605
 95 | 2022-11-18,2.2165
 96 | 2022-11-25,2.1585
 97 | 2022-12-02,2.0965
 98 | 2022-12-09,2.067
 99 | 2022-12-16,2.058
100 | 2022-12-23,2.0925
101 | 2022-12-30,2.078
102 | 2023-01-06,2.2335
103 | 2023-01-13,2.288
104 | 2023-01-20,2.1805
105 | 2023-01-27,2.351
106 | 2023-02-03,2.383
107 | 2023-02-10,2.477
108 | 2023-02-17,2.5645
109 | 2023-02-24,2.495
110 | 2023-03-03,2.5745
111 | 2023-03-10,2.482
112 | 2023-03-17,2.193
113 | 2023-03-24,2.263
114 | 2023-03-31,2.3675
115 | 2023-04-14,2.4685
116 | 2023-04-21,2.4845
117 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "timexseries"
 3 | version = "1.2.3"
 4 | description = "TIMEX is a framework for time-series-forecasting-as-a-service."
 5 | authors = ["Alessandro Falcetta <alessandro.falcetta@polimi.it>", "Manuel Roveri <manuel.roveri@polimi.it>"]
 6 | license = "Apache-2.0"
 7 | 
 8 | readme = "README.md"
 9 | 
10 | repository = "https://github.com/AlexMV12/TIMEX"
11 | homepage = "https://alexmv12.github.io/TIMEX/"
12 | 
13 | keywords = ["time-series", "time", "forecasting", "as-a-service"]
14 | 
15 | classifiers = [
16 |     "Topic :: Scientific/Engineering :: Mathematics",
17 |     "Topic :: Scientific/Engineering :: Information Analysis"
18 | ]
19 | 
20 | 
21 | [tool.poetry.dependencies]
22 | python = ">=3.8,<3.11"
23 | dateparser = "^1.1.0"
24 | gunicorn = "^20.1.0"
25 | pandas = "^1.5.0"
26 | joblib = "^1.2.0"
27 | holidays = "0.24"
28 | numpy = "1.23.5"
29 | 
30 | scipy = { version = ">=1.10.1", optional = true}
31 | dash = { version = ">=2.0.0", optional = true}
32 | dash-bootstrap-components = { version = ">=1.0.3", optional = true}
33 | colorhash = { version = ">=1.0.4", optional = true}
34 | networkx = { version = ">=2.6.3", optional = true}
35 | statsforecast = { version = ">=1.0.0", optional = true}
36 | scikit-learn = { version = ">=1.2.1", optional = true}
37 | statsmodels = { version = ">=0.13.1", optional = true}
38 | prophet = { version = "1.1.1", optional = true}
39 | 
40 | 
41 | [tool.poetry.extras]
42 | data_prediction = ["prophet", "scipy", "statsmodels", "statsforecast", "scikit-learn"]
43 | data_visualization = ["dash", "dash-bootstrap-components", "colorhash", "networkx", "statsmodels"]
44 | 
45 | 
46 | [tool.poetry.group.dev.dependencies]
47 | pytest = "^6.2.2"
48 | pytest-cov = "^2.11.1"
49 | coverage-badge = "^1.0.1"
50 | pdoc3 = "^0.9.2"
51 | 
52 | [build-system]
53 | requires = ["poetry-core"]
54 | build-backend = "poetry.core.masonry.api"
55 | 


--------------------------------------------------------------------------------
/tests/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/DhiriaDev/TIMEX/767841a830bdc6fafbe1702700b9f80175e357de/tests/__init__.py


--------------------------------------------------------------------------------
/tests/context.py:
--------------------------------------------------------------------------------
1 | import os
2 | import sys
3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
4 | 
5 | import timexseries


--------------------------------------------------------------------------------
/tests/launch_tests.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | 
3 | rm .coverage
4 | coverage run --source=../timexseries/ -m pytest .
5 | coverage-badge -f -o ../badges/coverage.svg


--------------------------------------------------------------------------------
/tests/test_data_visualization.py:
--------------------------------------------------------------------------------
 1 | import pickle
 2 | import pytest
 3 | from timexseries.data_visualization import create_timeseries_dash_children
 4 | 
 5 | from timexseries.data_prediction import create_timeseries_containers
 6 | 
 7 | from timexseries.data_ingestion import ingest_timeseries
 8 | 
 9 | # Testing data visualization is tricky.
10 | # Pre-computed Dash children are saved in a sample pkl file; these visualizations have been checked by a human.
11 | # Hence, a rudimental way to test data visualization is to check that the TIMEX code actually produces those plots,
12 | # by comparing the fresh created ones with the ones saved before.
13 | 
14 | # Cant test cross-correlation graphs because they are random initialized.
15 | 
16 | 
17 | @pytest.fixture
18 | def dash_children(tmp_path):
19 |     param_config = {
20 |         "activity_title": "Example",
21 |         "verbose": "INFO",
22 |         "input_parameters": {
23 |             "source_data_url": "test_datasets/data_visualization/test1.csv",
24 |             "datetime_column_name": "ind",
25 |             "index_column_name": "ind",
26 |             "frequency": "D",
27 |         },
28 |         "model_parameters": {
29 |             "validation_values": 3,
30 |             "delta_training_percentage": 30,
31 |             "forecast_horizon": 5,
32 |             "possible_transformations": "none,log_modified",
33 |             "models": "mockup",
34 |             "main_accuracy_estimator": "mae"
35 |         },
36 |         "historical_prediction_parameters": {
37 |             "initial_index": "2000-01-15",
38 |             "save_path": f"{tmp_path}/historical_predictions.pkl"
39 |         },
40 |         "visualization_parameters": {
41 |             "xcorr_graph_threshold": 0.8,
42 |             "box_plot_frequency": "1W"
43 |         }
44 |     }
45 | 
46 |     ingested_dataset = ingest_timeseries(param_config)[2]
47 |     timeseries_containers = create_timeseries_containers(ingested_dataset, param_config)
48 | 
49 |     # Data visualization
50 |     children_for_each_timeseries = [{
51 |         'name': s.timeseries_data.columns[0],
52 |         'children': create_timeseries_dash_children(s, param_config)
53 |     } for s in timeseries_containers]
54 | 
55 |     return children_for_each_timeseries
56 | 
57 | 
58 | @pytest.fixture
59 | def expected_children():
60 |     with open("test_datasets/data_visualization/expected_children1.pkl", "rb") as file:
61 |         expected_children = pickle.load(file)
62 | 
63 |     return expected_children
64 | 
65 | 
66 | def test_1(dash_children, expected_children):
67 |     for computed, expected in zip(dash_children[0]['children'], expected_children[0]['children']):
68 |         assert computed.__str__() == expected.__str__()
69 | 
70 | 
71 | 
72 | 
73 | 
74 | 
75 | 
76 | 
77 | 
78 | 
79 | 


--------------------------------------------------------------------------------
/tests/test_datasets/data_visualization/create_demo.py:
--------------------------------------------------------------------------------
 1 | import pickle
 2 | from datetime import datetime, timezone
 3 | 
 4 | import dash
 5 | from dash import html
 6 | from datetime import datetime, timezone
 7 | from dash import dcc
 8 | import dateparser
 9 | from dash.dependencies import Input, Output
10 | from pandas import read_csv
11 | 
12 | from timexseries.data_ingestion import ingest_timeseries
13 | from timexseries.data_prediction import create_timeseries_containers
14 | from timexseries.data_visualization import create_timeseries_dash_children
15 | 
16 | param_config = {
17 |         "activity_title": "Example",
18 |         "verbose": "INFO",
19 |         "input_parameters": {
20 |             "source_data_url": "test1.csv",
21 |             "datetime_column_name": "ind",
22 |             "index_column_name": "ind",
23 |             "frequency": "D",
24 |         },
25 |         "model_parameters": {
26 |             "test_values": 3,
27 |             "delta_training_percentage": 30,
28 |             "prediction_lags": 5,
29 |             "possible_transformations": "none,log_modified",
30 |             "models": "mockup",
31 |             "main_accuracy_estimator": "mae"
32 |         },
33 |         "historical_prediction_parameters": {
34 |             "initial_index": "2000-01-15",
35 |             "save_path": f"historical_predictions.pkl"
36 |         },
37 |         "visualization_parameters": {
38 |             "xcorr_graph_threshold": 0.8,
39 |             "box_plot_frequency": "1W"
40 |         }
41 |     }
42 | 
43 | ingested_dataset = ingest_timeseries(param_config)
44 | timeseries_containers = create_timeseries_containers(ingested_dataset, param_config)
45 | 
46 | # Data visualization
47 | children_for_each_timeseries = [{
48 |     'name': s.timeseries_data.columns[0],
49 |     'children': create_timeseries_dash_children(s, param_config)
50 | } for s in timeseries_containers]
51 | 
52 | with open("expected_children1.pkl", 'wb') as f:
53 |     pickle.dump(children_for_each_timeseries, f)
54 | 
55 | # Initialize Dash app.
56 | app = dash.Dash(__name__)
57 | server = app.server
58 | 
59 | now = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
60 | 
61 | disclaimer = [html.Div([
62 |     html.H1("Test"),
63 |     html.Hr(),
64 |     dcc.Markdown('''
65 |         Test
66 |         '''),
67 |     html.Div("Last updated at (yyyy-mm-dd, UTC time): " + str(now)),
68 |     html.Br(),
69 |     html.H2("Please select the data of interest:")
70 | ], style={'width': '80%', 'margin': 'auto'}
71 | ), dcc.Dropdown(
72 |     id='timeseries_selector',
73 |     options=[{'label': i['name'], 'value': i['name']} for i in children_for_each_timeseries],
74 |     value='Time-series'
75 | ), html.Div(id="timeseries_wrapper"), html.Div(dcc.Graph(), style={'display': 'none'})]
76 | tree = html.Div(children=disclaimer, style={'width': '80%', 'margin': 'auto'})
77 | 
78 | app.layout = tree
79 | 
80 | 
81 | @app.callback(
82 |     Output(component_id='timeseries_wrapper', component_property='children'),
83 |     [Input(component_id='timeseries_selector', component_property='value')]
84 | )
85 | def update_timeseries_wrapper(input_value):
86 |     try:
87 |         children = next(x['children'] for x in children_for_each_timeseries if x['name'] == input_value)
88 |     except StopIteration:
89 |         return html.Div(style={'padding': 200})
90 | 
91 |     return children
92 | 
93 | 
94 | app.run_server()
95 | 


--------------------------------------------------------------------------------
/tests/test_datasets/data_visualization/expected_children1.pkl:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/DhiriaDev/TIMEX/767841a830bdc6fafbe1702700b9f80175e357de/tests/test_datasets/data_visualization/expected_children1.pkl


--------------------------------------------------------------------------------
/tests/test_datasets/data_visualization/test1.csv:
--------------------------------------------------------------------------------
 1 | ind,A,B
 2 | 2000-01-01,1,11
 3 | 2000-01-02,2,12
 4 | 2000-01-03,3,13
 5 | 2000-01-04,4,14
 6 | 2000-01-05,5,15
 7 | 2000-01-06,6,16
 8 | 2000-01-07,7,17
 9 | 2000-01-08,8,18
10 | 2000-01-09,9,19
11 | 2000-01-10,10,20
12 | 2000-01-11,11,21
13 | 2000-01-12,12,22
14 | 2000-01-13,13,23
15 | 2000-01-14,14,24
16 | 2000-01-15,15,25
17 | 2000-01-16,16,26
18 | 2000-01-17,17,27
19 | 2000-01-18,18,28
20 | 2000-01-19,19,29


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<BusinessDay>_BrentOil.csv:
--------------------------------------------------------------------------------
  1 | Date,Close/Last
  2 | 04/11/2023,42
  3 | 04/10/2023,42
  4 | 04/06/2023,42
  5 | 04/05/2023,42
  6 | 04/04/2023,42
  7 | 04/03/2023,42
  8 | 03/31/2023,42
  9 | 03/30/2023,42
 10 | 03/29/2023,42
 11 | 03/28/2023,42
 12 | 03/27/2023,42
 13 | 03/24/2023,42
 14 | 03/23/2023,42
 15 | 03/22/2023,42
 16 | 03/21/2023,42
 17 | 03/20/2023,42
 18 | 03/17/2023,42
 19 | 03/16/2023,42
 20 | 03/15/2023,42
 21 | 03/14/2023,42
 22 | 03/13/2023,42
 23 | 03/10/2023,42
 24 | 03/09/2023,42
 25 | 03/08/2023,42
 26 | 03/07/2023,42
 27 | 03/06/2023,42
 28 | 03/03/2023,42
 29 | 03/02/2023,42
 30 | 03/01/2023,42
 31 | 02/28/2023,42
 32 | 02/27/2023,42
 33 | 02/24/2023,42
 34 | 02/23/2023,42
 35 | 02/22/2023,42
 36 | 02/21/2023,42
 37 | 02/17/2023,42
 38 | 02/16/2023,42
 39 | 02/15/2023,42
 40 | 02/14/2023,42
 41 | 02/13/2023,42
 42 | 02/10/2023,42
 43 | 02/09/2023,42
 44 | 02/08/2023,42
 45 | 02/07/2023,42
 46 | 02/06/2023,42
 47 | 02/03/2023,42
 48 | 02/02/2023,42
 49 | 02/01/2023,42
 50 | 01/31/2023,42
 51 | 01/30/2023,42
 52 | 01/27/2023,42
 53 | 01/26/2023,42
 54 | 01/25/2023,42
 55 | 01/24/2023,42
 56 | 01/23/2023,42
 57 | 01/20/2023,42
 58 | 01/19/2023,42
 59 | 01/18/2023,42
 60 | 01/17/2023,42
 61 | 01/13/2023,42
 62 | 01/12/2023,42
 63 | 01/11/2023,42
 64 | 01/10/2023,42
 65 | 01/09/2023,42
 66 | 01/06/2023,42
 67 | 01/05/2023,42
 68 | 01/04/2023,42
 69 | 01/03/2023,42
 70 | 12/30/2022,42
 71 | 12/29/2022,42
 72 | 12/28/2022,42
 73 | 12/27/2022,42
 74 | 12/23/2022,42
 75 | 12/22/2022,42
 76 | 12/21/2022,42
 77 | 12/20/2022,42
 78 | 12/19/2022,42
 79 | 12/16/2022,42
 80 | 12/15/2022,42
 81 | 12/14/2022,42
 82 | 12/13/2022,42
 83 | 12/12/2022,42
 84 | 12/09/2022,42
 85 | 12/08/2022,42
 86 | 12/07/2022,42
 87 | 12/06/2022,42
 88 | 12/05/2022,42
 89 | 12/02/2022,42
 90 | 12/01/2022,42
 91 | 11/30/2022,42
 92 | 11/29/2022,42
 93 | 11/28/2022,42
 94 | 11/25/2022,42
 95 | 11/23/2022,42
 96 | 11/22/2022,42
 97 | 11/21/2022,42
 98 | 11/18/2022,42
 99 | 11/17/2022,42
100 | 11/16/2022,42
101 | 11/15/2022,42
102 | 11/14/2022,42
103 | 11/11/2022,42
104 | 11/10/2022,42
105 | 11/09/2022,42
106 | 11/08/2022,42
107 | 11/07/2022,42
108 | 11/04/2022,42
109 | 11/03/2022,42
110 | 11/02/2022,42
111 | 11/01/2022,42
112 | 10/31/2022,42
113 | 10/28/2022,42
114 | 10/27/2022,42
115 | 10/26/2022,42
116 | 10/25/2022,42
117 | 10/24/2022,42
118 | 10/21/2022,42
119 | 10/20/2022,42
120 | 10/19/2022,42
121 | 10/18/2022,42
122 | 10/17/2022,42
123 | 10/14/2022,42
124 | 10/13/2022,42
125 | 10/12/2022,42
126 | 10/11/2022,42
127 | 10/10/2022,42
128 | 10/07/2022,42
129 | 10/06/2022,42
130 | 10/05/2022,42
131 | 10/04/2022,42
132 | 10/03/2022,42
133 | 09/30/2022,42
134 | 09/29/2022,42
135 | 09/28/2022,42
136 | 09/27/2022,42
137 | 09/26/2022,42
138 | 09/23/2022,42
139 | 09/22/2022,42
140 | 09/21/2022,42
141 | 09/20/2022,42
142 | 09/19/2022,42
143 | 09/16/2022,42
144 | 09/15/2022,42
145 | 09/14/2022,42
146 | 09/13/2022,42
147 | 09/12/2022,42
148 | 09/09/2022,42
149 | 09/08/2022,42
150 | 09/07/2022,42
151 | 09/06/2022,42
152 | 09/02/2022,42
153 | 09/01/2022,42
154 | 08/31/2022,42
155 | 08/30/2022,42
156 | 08/29/2022,42
157 | 08/26/2022,42
158 | 08/25/2022,42
159 | 08/24/2022,42
160 | 08/23/2022,42
161 | 08/22/2022,42
162 | 08/19/2022,42
163 | 08/18/2022,42
164 | 08/17/2022,42
165 | 08/16/2022,42
166 | 08/15/2022,42
167 | 08/12/2022,42
168 | 08/11/2022,42
169 | 08/10/2022,42
170 | 08/09/2022,42
171 | 08/08/2022,42
172 | 08/05/2022,42
173 | 08/04/2022,42
174 | 08/03/2022,42
175 | 08/02/2022,42
176 | 08/01/2022,42
177 | 07/29/2022,42
178 | 07/28/2022,42
179 | 07/27/2022,42
180 | 07/26/2022,42
181 | 07/25/2022,42
182 | 07/22/2022,42
183 | 07/21/2022,42
184 | 07/20/2022,42
185 | 07/19/2022,42
186 | 07/18/2022,42
187 | 07/15/2022,42
188 | 07/14/2022,42
189 | 07/13/2022,42
190 | 07/12/2022,42
191 | 07/11/2022,42
192 | 07/08/2022,42
193 | 07/07/2022,42
194 | 07/06/2022,42
195 | 07/05/2022,42
196 | 07/01/2022,42
197 | 06/30/2022,42
198 | 06/29/2022,42
199 | 06/28/2022,42
200 | 06/27/2022,42
201 | 06/24/2022,42
202 | 06/23/2022,42
203 | 06/22/2022,42
204 | 06/21/2022,42
205 | 06/17/2022,42
206 | 06/16/2022,42
207 | 06/15/2022,42
208 | 06/14/2022,42
209 | 06/13/2022,42
210 | 06/10/2022,42
211 | 06/09/2022,42
212 | 06/08/2022,42
213 | 06/07/2022,42
214 | 06/06/2022,42
215 | 06/03/2022,42
216 | 06/02/2022,42
217 | 06/01/2022,42
218 | 05/31/2022,42
219 | 05/27/2022,42
220 | 05/26/2022,42
221 | 05/25/2022,42
222 | 05/24/2022,42
223 | 05/23/2022,42
224 | 05/20/2022,42
225 | 05/19/2022,42
226 | 05/18/2022,42
227 | 05/17/2022,42
228 | 05/16/2022,42
229 | 05/13/2022,42
230 | 05/12/2022,42
231 | 05/11/2022,42
232 | 05/10/2022,42
233 | 05/09/2022,42
234 | 05/06/2022,42
235 | 05/05/2022,42
236 | 05/04/2022,42
237 | 05/03/2022,42
238 | 05/02/2022,42
239 | 04/29/2022,42
240 | 04/28/2022,42
241 | 04/27/2022,42
242 | 04/26/2022,42
243 | 04/25/2022,42
244 | 04/22/2022,42
245 | 04/21/2022,42
246 | 04/20/2022,42
247 | 04/19/2022,42
248 | 04/18/2022,42
249 | 04/14/2022,42
250 | 04/13/2022,42
251 | 04/12/2022,42
252 | 04/11/2022,42
253 | 04/08/2022,42
254 | 04/07/2022,42
255 | 04/06/2022,42
256 | 04/05/2022,42
257 | 04/04/2022,42
258 | 04/01/2022,42
259 | 03/31/2022,42
260 | 03/30/2022,42
261 | 03/29/2022,42
262 | 03/28/2022,42
263 | 03/25/2022,42
264 | 03/24/2022,42
265 | 03/23/2022,42
266 | 03/22/2022,42
267 | 03/21/2022,42
268 | 03/18/2022,42
269 | 03/17/2022,42
270 | 03/16/2022,42
271 | 03/15/2022,42
272 | 03/14/2022,42
273 | 03/11/2022,42
274 | 03/10/2022,42
275 | 03/09/2022,42
276 | 03/08/2022,42
277 | 03/07/2022,42
278 | 03/04/2022,42
279 | 03/03/2022,42
280 | 03/02/2022,42
281 | 03/01/2022,42
282 | 02/28/2022,42
283 | 02/25/2022,42
284 | 02/24/2022,42
285 | 02/23/2022,42
286 | 02/22/2022,42
287 | 02/18/2022,42
288 | 02/17/2022,42
289 | 02/16/2022,42
290 | 02/15/2022,42
291 | 02/14/2022,42
292 | 02/11/2022,42
293 | 02/10/2022,42
294 | 02/09/2022,42
295 | 02/08/2022,42
296 | 02/07/2022,42
297 | 02/04/2022,42
298 | 02/03/2022,42
299 | 02/02/2022,42
300 | 02/01/2022,42
301 | 01/31/2022,42
302 | 01/28/2022,42
303 | 01/27/2022,42
304 | 01/26/2022,42
305 | 01/25/2022,42
306 | 01/24/2022,42
307 | 01/21/2022,42
308 | 01/20/2022,42
309 | 01/19/2022,42
310 | 01/18/2022,42
311 | 01/14/2022,42
312 | 01/13/2022,42
313 | 01/12/2022,42
314 | 01/11/2022,42
315 | 01/10/2022,42
316 | 01/07/2022,42
317 | 01/06/2022,42
318 | 01/05/2022,42
319 | 01/04/2022,42
320 | 01/03/2022,42
321 | 12/31/2021,42
322 | 12/30/2021,42
323 | 12/29/2021,42
324 | 12/28/2021,42
325 | 12/27/2021,42
326 | 12/23/2021,42
327 | 12/22/2021,42
328 | 12/21/2021,42
329 | 12/20/2021,42
330 | 12/17/2021,42
331 | 12/16/2021,42
332 | 12/15/2021,42
333 | 12/14/2021,42
334 | 12/13/2021,42
335 | 12/10/2021,42
336 | 12/09/2021,42
337 | 12/08/2021,42
338 | 12/07/2021,42
339 | 12/06/2021,42
340 | 12/03/2021,42
341 | 12/02/2021,42
342 | 12/01/2021,42
343 | 11/30/2021,42
344 | 11/29/2021,42
345 | 11/26/2021,42
346 | 11/24/2021,42
347 | 11/23/2021,42
348 | 11/22/2021,42
349 | 11/19/2021,42
350 | 11/18/2021,42
351 | 11/17/2021,42
352 | 11/16/2021,42
353 | 11/15/2021,42
354 | 11/12/2021,42
355 | 11/11/2021,42
356 | 11/10/2021,42
357 | 11/09/2021,42
358 | 11/08/2021,42
359 | 11/05/2021,42
360 | 11/04/2021,42
361 | 11/03/2021,42
362 | 11/02/2021,42
363 | 11/01/2021,42
364 | 10/29/2021,42
365 | 10/28/2021,42
366 | 10/27/2021,42
367 | 10/26/2021,42
368 | 10/25/2021,42
369 | 10/22/2021,42
370 | 10/21/2021,42
371 | 10/20/2021,42
372 | 10/19/2021,42
373 | 10/18/2021,42
374 | 10/15/2021,42
375 | 10/14/2021,42
376 | 10/13/2021,42
377 | 10/12/2021,42
378 | 10/11/2021,42
379 | 10/08/2021,42
380 | 10/07/2021,42
381 | 10/06/2021,42
382 | 10/05/2021,42
383 | 10/04/2021,42
384 | 10/01/2021,42
385 | 09/30/2021,42
386 | 09/29/2021,42
387 | 09/28/2021,42
388 | 09/27/2021,42
389 | 09/24/2021,42
390 | 09/23/2021,42
391 | 09/22/2021,42
392 | 09/21/2021,42
393 | 09/20/2021,42
394 | 09/17/2021,42
395 | 09/16/2021,42
396 | 09/15/2021,42
397 | 09/14/2021,42
398 | 09/13/2021,42
399 | 09/10/2021,42
400 | 09/09/2021,42
401 | 09/08/2021,42
402 | 09/07/2021,42
403 | 09/03/2021,42
404 | 09/02/2021,42
405 | 09/01/2021,42
406 | 08/31/2021,42
407 | 08/30/2021,42
408 | 08/27/2021,42
409 | 08/26/2021,42
410 | 08/25/2021,42
411 | 08/24/2021,42
412 | 08/23/2021,42
413 | 08/20/2021,42
414 | 08/19/2021,42
415 | 08/18/2021,42
416 | 08/17/2021,42
417 | 08/16/2021,42
418 | 08/13/2021,42
419 | 08/12/2021,42
420 | 08/11/2021,42
421 | 08/10/2021,42
422 | 08/09/2021,42
423 | 08/06/2021,42
424 | 08/05/2021,42
425 | 08/04/2021,42
426 | 08/03/2021,42
427 | 08/02/2021,42
428 | 07/30/2021,42
429 | 07/29/2021,42
430 | 07/28/2021,42
431 | 07/27/2021,42
432 | 07/26/2021,42
433 | 07/23/2021,42
434 | 07/22/2021,42
435 | 07/21/2021,42
436 | 07/20/2021,42
437 | 07/19/2021,42
438 | 07/16/2021,42
439 | 07/15/2021,42
440 | 07/14/2021,42
441 | 07/13/2021,42
442 | 07/12/2021,42
443 | 07/09/2021,42
444 | 07/08/2021,42
445 | 07/07/2021,42
446 | 07/06/2021,42
447 | 07/02/2021,42
448 | 07/01/2021,42
449 | 06/30/2021,42
450 | 06/29/2021,42
451 | 06/28/2021,42
452 | 06/25/2021,42
453 | 06/24/2021,42
454 | 06/23/2021,42
455 | 06/22/2021,42
456 | 06/21/2021,42
457 | 06/18/2021,42
458 | 06/17/2021,42
459 | 06/16/2021,42
460 | 06/15/2021,42
461 | 06/14/2021,42
462 | 06/11/2021,42
463 | 06/10/2021,42
464 | 06/09/2021,42
465 | 06/08/2021,42
466 | 06/07/2021,42
467 | 06/04/2021,42
468 | 06/03/2021,42
469 | 06/02/2021,42
470 | 06/01/2021,42
471 | 05/28/2021,42
472 | 05/27/2021,42
473 | 05/26/2021,42
474 | 05/25/2021,42
475 | 05/24/2021,42
476 | 05/21/2021,42
477 | 05/20/2021,42
478 | 05/19/2021,42
479 | 05/18/2021,42
480 | 05/17/2021,42
481 | 05/14/2021,42
482 | 05/13/2021,42
483 | 05/12/2021,42
484 | 05/11/2021,42
485 | 05/10/2021,42
486 | 05/07/2021,42
487 | 05/06/2021,42
488 | 05/05/2021,42
489 | 05/04/2021,42
490 | 05/03/2021,42
491 | 04/30/2021,42
492 | 04/29/2021,42
493 | 04/28/2021,42
494 | 04/27/2021,42
495 | 04/26/2021,42
496 | 04/23/2021,42
497 | 04/22/2021,42
498 | 04/21/2021,42
499 | 04/20/2021,42
500 | 04/19/2021,42
501 | 04/16/2021,42
502 | 


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<BusinessDay>_Steel.csv:
--------------------------------------------------------------------------------
  1 | Date,Close
  2 | 03/27/23,42
  3 | 03/24/23,42
  4 | 03/23/23,42
  5 | 03/22/23,42
  6 | 03/21/23,42
  7 | 03/20/23,42
  8 | 03/17/23,42
  9 | 03/16/23,42
 10 | 03/15/23,42
 11 | 03/14/23,42
 12 | 03/13/23,42
 13 | 03/10/23,42
 14 | 03/09/23,42
 15 | 03/08/23,42
 16 | 03/07/23,42
 17 | 03/06/23,42
 18 | 03/03/23,42
 19 | 03/02/23,42
 20 | 03/01/23,42
 21 | 02/28/23,42
 22 | 02/27/23,42
 23 | 02/24/23,42
 24 | 02/23/23,42
 25 | 02/22/23,42
 26 | 02/21/23,42
 27 | 02/17/23,42
 28 | 02/16/23,42
 29 | 02/15/23,42
 30 | 02/14/23,42
 31 | 02/13/23,42
 32 | 02/10/23,42
 33 | 02/09/23,42
 34 | 02/08/23,42
 35 | 02/07/23,42
 36 | 02/06/23,42
 37 | 02/03/23,42
 38 | 02/02/23,42
 39 | 02/01/23,42
 40 | 01/31/23,42
 41 | 01/30/23,42
 42 | 01/27/23,42
 43 | 01/26/23,42
 44 | 01/25/23,42
 45 | 01/24/23,42
 46 | 01/23/23,42
 47 | 01/20/23,42
 48 | 01/19/23,42
 49 | 01/18/23,42
 50 | 01/17/23,42
 51 | 01/13/23,42
 52 | 01/12/23,42
 53 | 01/11/23,42
 54 | 01/10/23,42
 55 | 01/09/23,42
 56 | 01/06/23,42
 57 | 01/05/23,42
 58 | 01/04/23,42
 59 | 01/03/23,42
 60 | 12/30/22,42
 61 | 12/29/22,42
 62 | 12/28/22,42
 63 | 12/27/22,42
 64 | 12/23/22,42
 65 | 12/22/22,42
 66 | 12/21/22,42
 67 | 12/20/22,42
 68 | 12/19/22,42
 69 | 12/16/22,42
 70 | 12/15/22,42
 71 | 12/14/22,42
 72 | 12/13/22,42
 73 | 12/12/22,42
 74 | 12/09/22,42
 75 | 12/08/22,42
 76 | 12/07/22,42
 77 | 12/06/22,42
 78 | 12/05/22,42
 79 | 12/02/22,42
 80 | 12/01/22,42
 81 | 11/30/22,42
 82 | 11/29/22,42
 83 | 11/28/22,42
 84 | 11/25/22,42
 85 | 11/23/22,42
 86 | 11/22/22,42
 87 | 11/21/22,42
 88 | 11/18/22,42
 89 | 11/17/22,42
 90 | 11/16/22,42
 91 | 11/15/22,42
 92 | 11/14/22,42
 93 | 11/11/22,42
 94 | 11/10/22,42
 95 | 11/09/22,42
 96 | 11/08/22,42
 97 | 11/07/22,42
 98 | 11/04/22,42
 99 | 11/03/22,42
100 | 11/02/22,42
101 | 11/01/22,42
102 | 10/31/22,42
103 | 10/28/22,42
104 | 10/27/22,42
105 | 10/26/22,42
106 | 10/25/22,42
107 | 10/24/22,42
108 | 10/21/22,42
109 | 10/20/22,42
110 | 10/19/22,42
111 | 10/18/22,42
112 | 10/17/22,42
113 | 10/14/22,42
114 | 10/13/22,42
115 | 10/12/22,42
116 | 10/11/22,42
117 | 10/10/22,42
118 | 10/07/22,42
119 | 10/06/22,42
120 | 10/05/22,42
121 | 10/04/22,42
122 | 10/03/22,42
123 | 09/30/22,42
124 | 09/29/22,42
125 | 09/28/22,42
126 | 09/27/22,42
127 | 09/26/22,42
128 | 09/23/22,42
129 | 09/22/22,42
130 | 09/21/22,42
131 | 09/20/22,42
132 | 09/19/22,42
133 | 09/16/22,42
134 | 09/15/22,42
135 | 09/14/22,42
136 | 09/13/22,42
137 | 09/12/22,42
138 | 09/09/22,42
139 | 09/08/22,42
140 | 09/07/22,42
141 | 09/06/22,42
142 | 09/02/22,42
143 | 09/01/22,42
144 | 08/31/22,42
145 | 08/30/22,42
146 | 08/29/22,42
147 | 08/26/22,42
148 | 08/25/22,42
149 | 08/24/22,42
150 | 08/23/22,42
151 | 08/22/22,42
152 | 08/19/22,42
153 | 08/18/22,42
154 | 08/17/22,42
155 | 08/16/22,42
156 | 08/15/22,42
157 | 08/12/22,42
158 | 08/11/22,42
159 | 08/10/22,42
160 | 08/09/22,42
161 | 08/08/22,42
162 | 08/05/22,42
163 | 08/04/22,42
164 | 08/03/22,42
165 | 08/02/22,42
166 | 08/01/22,42
167 | 07/29/22,42
168 | 07/28/22,42
169 | 07/27/22,42
170 | 07/26/22,42
171 | 07/25/22,42
172 | 07/22/22,42
173 | 07/21/22,42
174 | 07/20/22,42
175 | 07/19/22,42
176 | 07/18/22,42
177 | 07/15/22,42
178 | 07/14/22,42
179 | 07/13/22,42
180 | 07/12/22,42
181 | 07/11/22,42
182 | 07/08/22,42
183 | 07/07/22,42
184 | 07/06/22,42
185 | 07/05/22,42
186 | 07/01/22,42
187 | 06/30/22,42
188 | 06/29/22,42
189 | 06/28/22,42
190 | 06/27/22,42
191 | 06/24/22,42
192 | 06/23/22,42
193 | 06/22/22,42
194 | 06/21/22,42
195 | 06/17/22,42
196 | 06/16/22,42
197 | 06/15/22,42
198 | 06/14/22,42
199 | 06/13/22,42
200 | 06/10/22,42
201 | 06/09/22,42
202 | 06/08/22,42
203 | 06/07/22,42
204 | 06/06/22,42
205 | 06/03/22,42
206 | 06/02/22,42
207 | 06/01/22,42
208 | 05/31/22,42
209 | 05/27/22,42
210 | 05/26/22,42
211 | 05/25/22,42
212 | 05/24/22,42
213 | 05/23/22,42
214 | 05/20/22,42
215 | 05/19/22,42
216 | 05/18/22,42
217 | 05/17/22,42
218 | 05/16/22,42
219 | 05/13/22,42
220 | 05/12/22,42
221 | 05/11/22,42
222 | 05/10/22,42
223 | 05/09/22,42
224 | 05/06/22,42
225 | 05/05/22,42
226 | 05/04/22,42
227 | 05/03/22,42
228 | 05/02/22,42
229 | 04/29/22,42
230 | 04/28/22,42
231 | 04/27/22,42
232 | 04/26/22,42
233 | 04/25/22,42
234 | 04/22/22,42
235 | 04/21/22,42
236 | 04/20/22,42
237 | 04/19/22,42
238 | 04/18/22,42
239 | 04/14/22,42
240 | 04/13/22,42
241 | 04/12/22,42
242 | 04/11/22,42
243 | 04/08/22,42
244 | 04/07/22,42
245 | 04/06/22,42
246 | 04/05/22,42
247 | 04/04/22,42
248 | 04/01/22,42
249 | 03/31/22,42
250 | 03/30/22,42
251 | 03/29/22,42
252 | 03/28/22,42
253 | 03/25/22,42
254 | 03/24/22,42
255 | 03/23/22,42
256 | 03/22/22,42
257 | 03/21/22,42
258 | 03/18/22,42
259 | 03/17/22,42
260 | 03/16/22,42
261 | 03/15/22,42
262 | 03/14/22,42
263 | 03/11/22,42
264 | 03/10/22,42
265 | 03/09/22,42
266 | 03/08/22,42
267 | 03/07/22,42
268 | 03/04/22,42
269 | 03/03/22,42
270 | 03/02/22,42
271 | 03/01/22,42
272 | 02/28/22,42
273 | 02/25/22,42
274 | 02/24/22,42
275 | 02/23/22,42
276 | 02/22/22,42
277 | 02/18/22,42
278 | 02/17/22,42
279 | 02/16/22,42
280 | 02/15/22,42
281 | 02/14/22,42
282 | 02/11/22,42
283 | 02/10/22,42
284 | 02/09/22,42
285 | 02/08/22,42
286 | 02/07/22,42
287 | 02/04/22,42
288 | 02/03/22,42
289 | 02/02/22,42
290 | 02/01/22,42
291 | 01/31/22,42
292 | 01/28/22,42
293 | 01/27/22,42
294 | 01/26/22,42
295 | 01/25/22,42
296 | 01/24/22,42
297 | 01/21/22,42
298 | 01/20/22,42
299 | 01/19/22,42
300 | 01/18/22,42
301 | 01/14/22,42
302 | 01/13/22,42
303 | 01/12/22,42
304 | 01/11/22,42
305 | 01/10/22,42
306 | 01/07/22,42
307 | 01/06/22,42
308 | 01/05/22,42
309 | 01/04/22,42
310 | 01/03/22,42
311 | 12/31/21,42
312 | 12/30/21,42
313 | 12/29/21,42
314 | 12/28/21,42
315 | 12/27/21,42
316 | 12/23/21,42
317 | 12/22/21,42
318 | 12/21/21,42
319 | 12/20/21,42
320 | 12/17/21,42
321 | 12/16/21,42
322 | 12/15/21,42
323 | 12/14/21,42
324 | 12/13/21,42
325 | 12/10/21,42
326 | 12/09/21,42
327 | 12/08/21,42
328 | 12/07/21,42
329 | 12/06/21,42
330 | 12/03/21,42
331 | 12/02/21,42
332 | 12/01/21,42
333 | 11/30/21,42
334 | 11/29/21,42
335 | 11/26/21,42
336 | 11/24/21,42
337 | 11/23/21,42
338 | 11/22/21,42
339 | 11/19/21,42
340 | 11/18/21,42
341 | 11/17/21,42
342 | 11/16/21,42
343 | 11/15/21,42
344 | 11/12/21,42
345 | 11/11/21,42
346 | 11/10/21,42
347 | 11/09/21,42
348 | 11/08/21,42
349 | 11/05/21,42
350 | 11/04/21,42
351 | 11/03/21,42
352 | 11/02/21,42
353 | 11/01/21,42
354 | 10/29/21,42
355 | 10/28/21,42
356 | 10/27/21,42
357 | 10/26/21,42
358 | 10/25/21,42
359 | 10/22/21,42
360 | 10/21/21,42
361 | 10/20/21,42
362 | 10/19/21,42
363 | 10/18/21,42
364 | 10/15/21,42
365 | 10/14/21,42
366 | 10/13/21,42
367 | 10/12/21,42
368 | 10/11/21,42
369 | 10/08/21,42
370 | 10/07/21,42
371 | 10/06/21,42
372 | 10/05/21,42
373 | 10/04/21,42
374 | 10/01/21,42
375 | 09/30/21,42
376 | 09/29/21,42
377 | 09/28/21,42
378 | 09/27/21,42
379 | 09/24/21,42
380 | 09/23/21,42
381 | 09/22/21,42
382 | 09/21/21,42
383 | 09/20/21,42
384 | 09/17/21,42
385 | 09/16/21,42
386 | 09/15/21,42
387 | 09/14/21,42
388 | 09/13/21,42
389 | 09/10/21,42
390 | 09/09/21,42
391 | 09/08/21,42
392 | 09/07/21,42
393 | 09/03/21,42
394 | 09/02/21,42
395 | 09/01/21,42
396 | 08/31/21,42
397 | 08/30/21,42
398 | 08/27/21,42
399 | 08/26/21,42
400 | 08/25/21,42
401 | 08/24/21,42
402 | 08/23/21,42
403 | 08/20/21,42
404 | 08/19/21,42
405 | 08/18/21,42
406 | 08/17/21,42
407 | 08/16/21,42
408 | 08/13/21,42
409 | 08/12/21,42
410 | 08/11/21,42
411 | 08/10/21,42
412 | 08/09/21,42
413 | 08/06/21,42
414 | 08/05/21,42
415 | 08/04/21,42
416 | 08/03/21,42
417 | 08/02/21,42
418 | 07/30/21,42
419 | 07/29/21,42
420 | 07/28/21,42
421 | 07/27/21,42
422 | 07/26/21,42
423 | 07/23/21,42
424 | 07/22/21,42
425 | 07/21/21,42
426 | 07/20/21,42
427 | 07/19/21,42
428 | 07/16/21,42
429 | 07/15/21,42
430 | 07/14/21,42
431 | 07/13/21,42
432 | 07/12/21,42
433 | 07/09/21,42
434 | 07/08/21,42
435 | 07/07/21,42
436 | 07/06/21,42
437 | 07/02/21,42
438 | 07/01/21,42
439 | 06/30/21,42
440 | 06/29/21,42
441 | 06/28/21,42
442 | 06/25/21,42
443 | 06/24/21,42
444 | 06/23/21,42
445 | 06/22/21,42
446 | 06/21/21,42
447 | 06/18/21,42
448 | 06/17/21,42
449 | 06/16/21,42
450 | 06/15/21,42
451 | 06/14/21,42
452 | 06/11/21,42
453 | 06/10/21,42
454 | 06/09/21,42
455 | 06/08/21,42
456 | 06/07/21,42
457 | 06/04/21,42
458 | 06/03/21,42
459 | 06/02/21,42
460 | 06/01/21,42
461 | 05/28/21,42
462 | 05/27/21,42
463 | 05/26/21,42
464 | 05/25/21,42
465 | 05/24/21,42
466 | 05/21/21,42
467 | 05/20/21,42
468 | 05/19/21,42
469 | 05/18/21,42
470 | 05/17/21,42
471 | 05/14/21,42
472 | 05/13/21,42
473 | 05/12/21,42
474 | 05/11/21,42
475 | 05/10/21,42
476 | 05/07/21,42
477 | 05/06/21,42
478 | 05/05/21,42
479 | 05/04/21,42
480 | 05/03/21,42
481 | 04/30/21,42
482 | 04/29/21,42
483 | 04/28/21,42
484 | 04/27/21,42
485 | 04/26/21,42
486 | 04/23/21,42
487 | 04/22/21,42
488 | 04/21/21,42
489 | 04/20/21,42
490 | 04/19/21,42
491 | 04/16/21,42
492 | 04/15/21,42
493 | 04/14/21,42
494 | 04/13/21,42
495 | 04/12/21,42
496 | 04/09/21,42
497 | 04/08/21,42
498 | 04/07/21,42
499 | 04/06/21,42
500 | 04/05/21,42
501 | 04/01/21,42
502 | 


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<Day>_Covid.csv:
--------------------------------------------------------------------------------
  1 | Data,Nuovi casi
  2 | 2020-02-25,42
  3 | 2020-02-26,42
  4 | 2020-02-27,42
  5 | 2020-02-28,42
  6 | 2020-02-29,42
  7 | 2020-03-01,42
  8 | 2020-03-02,42
  9 | 2020-03-03,42
 10 | 2020-03-04,42
 11 | 2020-03-05,42
 12 | 2020-03-06,42
 13 | 2020-03-07,42
 14 | 2020-03-08,42
 15 | 2020-03-09,42
 16 | 2020-03-10,42
 17 | 2020-03-11,42
 18 | 2020-03-12,42
 19 | 2020-03-13,42
 20 | 2020-03-14,42
 21 | 2020-03-15,42
 22 | 2020-03-16,42
 23 | 2020-03-17,42
 24 | 2020-03-18,42
 25 | 2020-03-19,42
 26 | 2020-03-20,42
 27 | 2020-03-21,42
 28 | 2020-03-22,42
 29 | 2020-03-23,42
 30 | 2020-03-24,42
 31 | 2020-03-25,42
 32 | 2020-03-26,42
 33 | 2020-03-27,42
 34 | 2020-03-28,42
 35 | 2020-03-29,42
 36 | 2020-03-30,42
 37 | 2020-03-31,42
 38 | 2020-04-01,42
 39 | 2020-04-02,42
 40 | 2020-04-03,42
 41 | 2020-04-04,42
 42 | 2020-04-05,42
 43 | 2020-04-06,42
 44 | 2020-04-07,42
 45 | 2020-04-08,42
 46 | 2020-04-09,42
 47 | 2020-04-10,42
 48 | 2020-04-11,42
 49 | 2020-04-12,42
 50 | 2020-04-13,42
 51 | 2020-04-14,42
 52 | 2020-04-15,42
 53 | 2020-04-16,42
 54 | 2020-04-17,42
 55 | 2020-04-18,42
 56 | 2020-04-19,42
 57 | 2020-04-20,42
 58 | 2020-04-21,42
 59 | 2020-04-22,42
 60 | 2020-04-23,42
 61 | 2020-04-24,42
 62 | 2020-04-25,42
 63 | 2020-04-26,42
 64 | 2020-04-27,42
 65 | 2020-04-28,42
 66 | 2020-04-29,42
 67 | 2020-04-30,42
 68 | 2020-05-01,42
 69 | 2020-05-02,42
 70 | 2020-05-03,42
 71 | 2020-05-04,42
 72 | 2020-05-05,42
 73 | 2020-05-06,42
 74 | 2020-05-07,42
 75 | 2020-05-08,42
 76 | 2020-05-09,42
 77 | 2020-05-10,42
 78 | 2020-05-11,42
 79 | 2020-05-12,42
 80 | 2020-05-13,42
 81 | 2020-05-14,42
 82 | 2020-05-15,42
 83 | 2020-05-16,42
 84 | 2020-05-17,42
 85 | 2020-05-18,42
 86 | 2020-05-19,42
 87 | 2020-05-20,42
 88 | 2020-05-21,42
 89 | 2020-05-22,42
 90 | 2020-05-23,42
 91 | 2020-05-24,42
 92 | 2020-05-25,42
 93 | 2020-05-26,42
 94 | 2020-05-27,42
 95 | 2020-05-28,42
 96 | 2020-05-29,42
 97 | 2020-05-30,42
 98 | 2020-05-31,42
 99 | 2020-06-01,42
100 | 2020-06-02,42
101 | 2020-06-03,42
102 | 2020-06-04,42
103 | 2020-06-05,42
104 | 2020-06-06,42
105 | 2020-06-07,42
106 | 2020-06-08,42
107 | 2020-06-09,42
108 | 2020-06-10,42
109 | 2020-06-11,42
110 | 2020-06-12,42
111 | 2020-06-13,42
112 | 2020-06-14,42
113 | 2020-06-15,42
114 | 2020-06-16,42
115 | 2020-06-17,42
116 | 2020-06-18,42
117 | 2020-06-19,42
118 | 2020-06-20,42
119 | 2020-06-21,42
120 | 2020-06-22,42
121 | 2020-06-23,42
122 | 2020-06-24,42
123 | 2020-06-25,42
124 | 2020-06-26,42
125 | 2020-06-27,42
126 | 2020-06-28,42
127 | 2020-06-29,42
128 | 2020-06-30,42
129 | 2020-07-01,42
130 | 2020-07-02,42
131 | 2020-07-03,42
132 | 2020-07-04,42
133 | 2020-07-05,42
134 | 2020-07-06,42
135 | 2020-07-07,42
136 | 2020-07-08,42
137 | 2020-07-09,42
138 | 2020-07-10,42
139 | 2020-07-11,42
140 | 2020-07-12,42
141 | 2020-07-13,42
142 | 2020-07-14,42
143 | 2020-07-15,42
144 | 2020-07-16,42
145 | 2020-07-17,42
146 | 2020-07-18,42
147 | 2020-07-19,42
148 | 2020-07-20,42
149 | 2020-07-21,42
150 | 2020-07-22,42
151 | 2020-07-23,42
152 | 2020-07-24,42
153 | 2020-07-25,42
154 | 2020-07-26,42
155 | 2020-07-27,42
156 | 2020-07-28,42
157 | 2020-07-29,42
158 | 2020-07-30,42
159 | 2020-07-31,42
160 | 2020-08-01,42
161 | 2020-08-02,42
162 | 2020-08-03,42
163 | 2020-08-04,42
164 | 2020-08-05,42
165 | 2020-08-06,42
166 | 2020-08-07,42
167 | 2020-08-08,42
168 | 2020-08-09,42
169 | 2020-08-10,42
170 | 2020-08-11,42
171 | 2020-08-12,42
172 | 2020-08-13,42
173 | 2020-08-14,42
174 | 2020-08-15,42
175 | 2020-08-16,42
176 | 2020-08-17,42
177 | 2020-08-18,42
178 | 2020-08-19,42
179 | 2020-08-20,42
180 | 2020-08-21,42
181 | 2020-08-22,42
182 | 2020-08-23,42
183 | 2020-08-24,42
184 | 2020-08-25,42
185 | 2020-08-26,42
186 | 2020-08-27,42
187 | 2020-08-28,42
188 | 2020-08-29,42
189 | 2020-08-30,42
190 | 2020-08-31,42
191 | 2020-09-01,42
192 | 2020-09-02,42
193 | 2020-09-03,42
194 | 2020-09-04,42
195 | 2020-09-05,42
196 | 2020-09-06,42
197 | 2020-09-07,42
198 | 2020-09-08,42
199 | 2020-09-09,42
200 | 2020-09-10,42
201 | 2020-09-11,42
202 | 2020-09-12,42
203 | 2020-09-13,42
204 | 2020-09-14,42
205 | 2020-09-15,42
206 | 2020-09-16,42
207 | 2020-09-17,42
208 | 2020-09-18,42
209 | 2020-09-19,42
210 | 2020-09-20,42
211 | 2020-09-21,42
212 | 2020-09-22,42
213 | 2020-09-23,42
214 | 2020-09-24,42
215 | 2020-09-25,42
216 | 2020-09-26,42
217 | 2020-09-27,42
218 | 2020-09-28,42
219 | 2020-09-29,42
220 | 2020-09-30,42
221 | 2020-10-01,42
222 | 2020-10-02,42
223 | 2020-10-03,42
224 | 2020-10-04,42
225 | 2020-10-05,42
226 | 2020-10-06,42
227 | 2020-10-07,42
228 | 2020-10-08,42
229 | 2020-10-09,42
230 | 2020-10-10,42
231 | 2020-10-11,42
232 | 2020-10-12,42
233 | 2020-10-13,42
234 | 2020-10-14,42
235 | 2020-10-15,42
236 | 2020-10-16,42
237 | 2020-10-17,42
238 | 2020-10-18,42
239 | 2020-10-19,42
240 | 2020-10-20,42
241 | 2020-10-21,42
242 | 2020-10-22,42
243 | 2020-10-23,42
244 | 2020-10-24,42
245 | 2020-10-25,42
246 | 2020-10-26,42
247 | 2020-10-27,42
248 | 2020-10-28,42
249 | 2020-10-29,42
250 | 2020-10-30,42
251 | 2020-10-31,42
252 | 2020-11-01,42
253 | 2020-11-02,42
254 | 2020-11-03,42
255 | 2020-11-04,42
256 | 2020-11-05,42
257 | 2020-11-06,42
258 | 2020-11-07,42
259 | 2020-11-08,42
260 | 2020-11-09,42
261 | 2020-11-10,42
262 | 2020-11-11,42
263 | 2020-11-12,42
264 | 2020-11-13,42
265 | 2020-11-14,42
266 | 2020-11-15,42
267 | 2020-11-16,42
268 | 2020-11-17,42
269 | 2020-11-18,42
270 | 2020-11-19,42
271 | 2020-11-20,42
272 | 2020-11-21,42
273 | 2020-11-22,42
274 | 2020-11-23,42
275 | 2020-11-24,42
276 | 2020-11-25,42
277 | 2020-11-26,42
278 | 2020-11-27,42
279 | 2020-11-28,42
280 | 2020-11-29,42
281 | 2020-11-30,42
282 | 2020-12-01,42
283 | 2020-12-02,42
284 | 2020-12-03,42
285 | 2020-12-04,42
286 | 2020-12-05,42
287 | 2020-12-06,42
288 | 2020-12-07,42
289 | 2020-12-08,42
290 | 2020-12-09,42
291 | 2020-12-10,42
292 | 2020-12-11,42
293 | 2020-12-12,42
294 | 2020-12-13,42
295 | 2020-12-14,42
296 | 2020-12-15,42
297 | 2020-12-16,42
298 | 2020-12-17,42
299 | 2020-12-18,42
300 | 2020-12-19,42
301 | 2020-12-20,42
302 | 2020-12-21,42
303 | 2020-12-22,42
304 | 2020-12-23,42
305 | 2020-12-24,42
306 | 2020-12-25,42
307 | 2020-12-26,42
308 | 2020-12-27,42
309 | 2020-12-28,42
310 | 2020-12-29,42
311 | 2020-12-30,42
312 | 2020-12-31,42
313 | 2021-01-01,42
314 | 2021-01-02,42
315 | 2021-01-03,42
316 | 2021-01-04,42
317 | 2021-01-05,42
318 | 2021-01-06,42
319 | 2021-01-07,42
320 | 2021-01-08,42
321 | 2021-01-09,42
322 | 2021-01-10,42
323 | 2021-01-11,42
324 | 2021-01-12,42
325 | 2021-01-13,42
326 | 2021-01-14,42
327 | 2021-01-15,42
328 | 2021-01-16,42
329 | 2021-01-17,42
330 | 2021-01-18,42
331 | 2021-01-19,42
332 | 2021-01-20,42
333 | 2021-01-21,42
334 | 2021-01-22,42
335 | 2021-01-23,42
336 | 2021-01-24,42
337 | 2021-01-25,42
338 | 2021-01-26,42
339 | 2021-01-27,42
340 | 2021-01-28,42
341 | 2021-01-29,42
342 | 2021-01-30,42
343 | 2021-01-31,42
344 | 2021-02-01,42
345 | 2021-02-02,42
346 | 2021-02-03,42
347 | 2021-02-04,42
348 | 2021-02-05,42
349 | 2021-02-06,42
350 | 2021-02-07,42
351 | 2021-02-08,42
352 | 2021-02-09,42
353 | 2021-02-10,42
354 | 2021-02-11,42
355 | 2021-02-12,42
356 | 2021-02-13,42
357 | 2021-02-14,42
358 | 2021-02-15,42
359 | 2021-02-16,42
360 | 2021-02-17,42
361 | 2021-02-18,42
362 | 2021-02-19,42
363 | 2021-02-20,42
364 | 2021-02-21,42
365 | 2021-02-22,42
366 | 2021-02-23,42
367 | 2021-02-24,42
368 | 2021-02-25,42
369 | 2021-02-26,42
370 | 2021-02-27,42
371 | 2021-02-28,42
372 | 2021-03-01,42
373 | 2021-03-02,42
374 | 2021-03-03,42
375 | 2021-03-04,42
376 | 2021-03-05,42
377 | 2021-03-06,42
378 | 2021-03-07,42
379 | 2021-03-08,42
380 | 2021-03-09,42
381 | 2021-03-10,42
382 | 2021-03-11,42
383 | 2021-03-12,42
384 | 2021-03-13,42
385 | 2021-03-14,42
386 | 2021-03-15,42
387 | 2021-03-16,42
388 | 2021-03-17,42
389 | 2021-03-18,42
390 | 2021-03-19,42
391 | 2021-03-20,42
392 | 2021-03-21,42
393 | 2021-03-22,42
394 | 2021-03-23,42
395 | 2021-03-24,42
396 | 2021-03-25,42
397 | 2021-03-26,42
398 | 2021-03-27,42
399 | 2021-03-28,42
400 | 2021-03-29,42
401 | 2021-03-30,42
402 | 2021-03-31,42
403 | 2021-04-01,42
404 | 2021-04-02,42
405 | 2021-04-03,42
406 | 2021-04-04,42
407 | 2021-04-05,42
408 | 2021-04-06,42
409 | 2021-04-07,42
410 | 2021-04-08,42
411 | 2021-04-09,42
412 | 2021-04-10,42
413 | 2021-04-11,42
414 | 2021-04-12,42
415 | 2021-04-13,42
416 | 2021-04-14,42
417 | 2021-04-15,42
418 | 2021-04-16,42
419 | 2021-04-17,42
420 | 2021-04-18,42
421 | 2021-04-19,42
422 | 2021-04-20,42
423 | 2021-04-21,42
424 | 2021-04-22,42
425 | 2021-04-23,42
426 | 2021-04-24,42
427 | 2021-04-25,42
428 | 2021-04-26,42
429 | 2021-04-27,42
430 | 2021-04-28,42
431 | 2021-04-29,42
432 | 2021-04-30,42
433 | 2021-05-01,42
434 | 2021-05-02,42
435 | 2021-05-03,42
436 | 2021-05-04,42
437 | 2021-05-05,42
438 | 2021-05-06,42
439 | 2021-05-07,42
440 | 2021-05-08,42
441 | 2021-05-09,42
442 | 2021-05-10,42
443 | 2021-05-11,42
444 | 2021-05-12,42
445 | 2021-05-13,42
446 | 2021-05-14,42
447 | 2021-05-15,42
448 | 2021-05-16,42
449 | 2021-05-17,42
450 | 2021-05-18,42
451 | 2021-05-19,42
452 | 2021-05-20,42
453 | 2021-05-21,42
454 | 2021-05-22,42
455 | 2021-05-23,42
456 | 2021-05-24,42
457 | 2021-05-25,42
458 | 2021-05-26,42
459 | 2021-05-27,42
460 | 2021-05-28,42
461 | 2021-05-29,42
462 | 2021-05-30,42
463 | 2021-05-31,42
464 | 2021-06-01,42
465 | 2021-06-02,42
466 | 2021-06-03,42
467 | 2021-06-04,42
468 | 2021-06-05,42
469 | 2021-06-06,42
470 | 2021-06-07,42
471 | 2021-06-08,42
472 | 2021-06-09,42
473 | 2021-06-10,42
474 | 2021-06-11,42
475 | 2021-06-12,42
476 | 2021-06-13,42
477 | 2021-06-14,42
478 | 2021-06-15,42
479 | 2021-06-16,42
480 | 2021-06-17,42
481 | 2021-06-18,42
482 | 2021-06-19,42
483 | 2021-06-20,42
484 | 2021-06-21,42
485 | 2021-06-22,42
486 | 2021-06-23,42
487 | 2021-06-24,42
488 | 2021-06-25,42
489 | 2021-06-26,42
490 | 2021-06-27,42
491 | 2021-06-28,42
492 | 2021-06-29,42
493 | 2021-06-30,42
494 | 2021-07-01,42
495 | 2021-07-02,42
496 | 2021-07-03,42
497 | 2021-07-04,42
498 | 2021-07-05,42
499 | 2021-07-06,42
500 | 2021-07-07,42
501 | 2021-07-08,42
502 | 


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<MonthBegin>_APU.csv:
--------------------------------------------------------------------------------
  1 | DATE,APU000072610
  2 | 1978-11-01,42
  3 | 1978-12-01,42
  4 | 1979-01-01,42
  5 | 1979-02-01,42
  6 | 1979-03-01,42
  7 | 1979-04-01,42
  8 | 1979-05-01,42
  9 | 1979-06-01,42
 10 | 1979-07-01,42
 11 | 1979-08-01,42
 12 | 1979-09-01,42
 13 | 1979-10-01,42
 14 | 1979-11-01,42
 15 | 1979-12-01,42
 16 | 1980-01-01,42
 17 | 1980-02-01,42
 18 | 1980-03-01,42
 19 | 1980-04-01,42
 20 | 1980-05-01,42
 21 | 1980-06-01,42
 22 | 1980-07-01,42
 23 | 1980-08-01,42
 24 | 1980-09-01,42
 25 | 1980-10-01,42
 26 | 1980-11-01,42
 27 | 1980-12-01,42
 28 | 1981-01-01,42
 29 | 1981-02-01,42
 30 | 1981-03-01,42
 31 | 1981-04-01,42
 32 | 1981-05-01,42
 33 | 1981-06-01,42
 34 | 1981-07-01,42
 35 | 1981-08-01,42
 36 | 1981-09-01,42
 37 | 1981-10-01,42
 38 | 1981-11-01,42
 39 | 1981-12-01,42
 40 | 1982-01-01,42
 41 | 1982-02-01,42
 42 | 1982-03-01,42
 43 | 1982-04-01,42
 44 | 1982-05-01,42
 45 | 1982-06-01,42
 46 | 1982-07-01,42
 47 | 1982-08-01,42
 48 | 1982-09-01,42
 49 | 1982-10-01,42
 50 | 1982-11-01,42
 51 | 1982-12-01,42
 52 | 1983-01-01,42
 53 | 1983-02-01,42
 54 | 1983-03-01,42
 55 | 1983-04-01,42
 56 | 1983-05-01,42
 57 | 1983-06-01,42
 58 | 1983-07-01,42
 59 | 1983-08-01,42
 60 | 1983-09-01,42
 61 | 1983-10-01,42
 62 | 1983-11-01,42
 63 | 1983-12-01,42
 64 | 1984-01-01,42
 65 | 1984-02-01,42
 66 | 1984-03-01,42
 67 | 1984-04-01,42
 68 | 1984-05-01,42
 69 | 1984-06-01,42
 70 | 1984-07-01,42
 71 | 1984-08-01,42
 72 | 1984-09-01,42
 73 | 1984-10-01,42
 74 | 1984-11-01,42
 75 | 1984-12-01,42
 76 | 1985-01-01,42
 77 | 1985-02-01,42
 78 | 1985-03-01,42
 79 | 1985-04-01,42
 80 | 1985-05-01,42
 81 | 1985-06-01,42
 82 | 1985-07-01,42
 83 | 1985-08-01,42
 84 | 1985-09-01,42
 85 | 1985-10-01,42
 86 | 1985-11-01,42
 87 | 1985-12-01,42
 88 | 1986-01-01,42
 89 | 1986-02-01,42
 90 | 1986-03-01,42
 91 | 1986-04-01,42
 92 | 1986-05-01,42
 93 | 1986-06-01,42
 94 | 1986-07-01,42
 95 | 1986-08-01,42
 96 | 1986-09-01,42
 97 | 1986-10-01,42
 98 | 1986-11-01,42
 99 | 1986-12-01,42
100 | 1987-01-01,42
101 | 1987-02-01,42
102 | 1987-03-01,42
103 | 1987-04-01,42
104 | 1987-05-01,42
105 | 1987-06-01,42
106 | 1987-07-01,42
107 | 1987-08-01,42
108 | 1987-09-01,42
109 | 1987-10-01,42
110 | 1987-11-01,42
111 | 1987-12-01,42
112 | 1988-01-01,42
113 | 1988-02-01,42
114 | 1988-03-01,42
115 | 1988-04-01,42
116 | 1988-05-01,42
117 | 1988-06-01,42
118 | 1988-07-01,42
119 | 1988-08-01,42
120 | 1988-09-01,42
121 | 1988-10-01,42
122 | 1988-11-01,42
123 | 1988-12-01,42
124 | 1989-01-01,42
125 | 1989-02-01,42
126 | 1989-03-01,42
127 | 1989-04-01,42
128 | 1989-05-01,42
129 | 1989-06-01,42
130 | 1989-07-01,42
131 | 1989-08-01,42
132 | 1989-09-01,42
133 | 1989-10-01,42
134 | 1989-11-01,42
135 | 1989-12-01,42
136 | 1990-01-01,42
137 | 1990-02-01,42
138 | 1990-03-01,42
139 | 1990-04-01,42
140 | 1990-05-01,42
141 | 1990-06-01,42
142 | 1990-07-01,42
143 | 1990-08-01,42
144 | 1990-09-01,42
145 | 1990-10-01,42
146 | 1990-11-01,42
147 | 1990-12-01,42
148 | 1991-01-01,42
149 | 1991-02-01,42
150 | 1991-03-01,42
151 | 1991-04-01,42
152 | 1991-05-01,42
153 | 1991-06-01,42
154 | 1991-07-01,42
155 | 1991-08-01,42
156 | 1991-09-01,42
157 | 1991-10-01,42
158 | 1991-11-01,42
159 | 1991-12-01,42
160 | 1992-01-01,42
161 | 1992-02-01,42
162 | 1992-03-01,42
163 | 1992-04-01,42
164 | 1992-05-01,42
165 | 1992-06-01,42
166 | 1992-07-01,42
167 | 1992-08-01,42
168 | 1992-09-01,42
169 | 1992-10-01,42
170 | 1992-11-01,42
171 | 1992-12-01,42
172 | 1993-01-01,42
173 | 1993-02-01,42
174 | 1993-03-01,42
175 | 1993-04-01,42
176 | 1993-05-01,42
177 | 1993-06-01,42
178 | 1993-07-01,42
179 | 1993-08-01,42
180 | 1993-09-01,42
181 | 1993-10-01,42
182 | 1993-11-01,42
183 | 1993-12-01,42
184 | 1994-01-01,42
185 | 1994-02-01,42
186 | 1994-03-01,42
187 | 1994-04-01,42
188 | 1994-05-01,42
189 | 1994-06-01,42
190 | 1994-07-01,42
191 | 1994-08-01,42
192 | 1994-09-01,42
193 | 1994-10-01,42
194 | 1994-11-01,42
195 | 1994-12-01,42
196 | 1995-01-01,42
197 | 1995-02-01,42
198 | 1995-03-01,42
199 | 1995-04-01,42
200 | 1995-05-01,42
201 | 1995-06-01,42
202 | 1995-07-01,42
203 | 1995-08-01,42
204 | 1995-09-01,42
205 | 1995-10-01,42
206 | 1995-11-01,42
207 | 1995-12-01,42
208 | 1996-01-01,42
209 | 1996-02-01,42
210 | 1996-03-01,42
211 | 1996-04-01,42
212 | 1996-05-01,42
213 | 1996-06-01,42
214 | 1996-07-01,42
215 | 1996-08-01,42
216 | 1996-09-01,42
217 | 1996-10-01,42
218 | 1996-11-01,42
219 | 1996-12-01,42
220 | 1997-01-01,42
221 | 1997-02-01,42
222 | 1997-03-01,42
223 | 1997-04-01,42
224 | 1997-05-01,42
225 | 1997-06-01,42
226 | 1997-07-01,42
227 | 1997-08-01,42
228 | 1997-09-01,42
229 | 1997-10-01,42
230 | 1997-11-01,42
231 | 1997-12-01,42
232 | 1998-01-01,42
233 | 1998-02-01,42
234 | 1998-03-01,42
235 | 1998-04-01,42
236 | 1998-05-01,42
237 | 1998-06-01,42
238 | 1998-07-01,42
239 | 1998-08-01,42
240 | 1998-09-01,42
241 | 1998-10-01,42
242 | 1998-11-01,42
243 | 1998-12-01,42
244 | 1999-01-01,42
245 | 1999-02-01,42
246 | 1999-03-01,42
247 | 1999-04-01,42
248 | 1999-05-01,42
249 | 1999-06-01,42
250 | 1999-07-01,42
251 | 1999-08-01,42
252 | 1999-09-01,42
253 | 1999-10-01,42
254 | 1999-11-01,42
255 | 1999-12-01,42
256 | 2000-01-01,42
257 | 2000-02-01,42
258 | 2000-03-01,42
259 | 2000-04-01,42
260 | 2000-05-01,42
261 | 2000-06-01,42
262 | 2000-07-01,42
263 | 2000-08-01,42
264 | 2000-09-01,42
265 | 2000-10-01,42
266 | 2000-11-01,42
267 | 2000-12-01,42
268 | 2001-01-01,42
269 | 2001-02-01,42
270 | 2001-03-01,42
271 | 2001-04-01,42
272 | 2001-05-01,42
273 | 2001-06-01,42
274 | 2001-07-01,42
275 | 2001-08-01,42
276 | 2001-09-01,42
277 | 2001-10-01,42
278 | 2001-11-01,42
279 | 2001-12-01,42
280 | 2002-01-01,42
281 | 2002-02-01,42
282 | 2002-03-01,42
283 | 2002-04-01,42
284 | 2002-05-01,42
285 | 2002-06-01,42
286 | 2002-07-01,42
287 | 2002-08-01,42
288 | 2002-09-01,42
289 | 2002-10-01,42
290 | 2002-11-01,42
291 | 2002-12-01,42
292 | 2003-01-01,42
293 | 2003-02-01,42
294 | 2003-03-01,42
295 | 2003-04-01,42
296 | 2003-05-01,42
297 | 2003-06-01,42
298 | 2003-07-01,42
299 | 2003-08-01,42
300 | 2003-09-01,42
301 | 2003-10-01,42
302 | 2003-11-01,42
303 | 2003-12-01,42
304 | 2004-01-01,42
305 | 2004-02-01,42
306 | 2004-03-01,42
307 | 2004-04-01,42
308 | 2004-05-01,42
309 | 2004-06-01,42
310 | 2004-07-01,42
311 | 2004-08-01,42
312 | 2004-09-01,42
313 | 2004-10-01,42
314 | 2004-11-01,42
315 | 2004-12-01,42
316 | 2005-01-01,42
317 | 2005-02-01,42
318 | 2005-03-01,42
319 | 2005-04-01,42
320 | 2005-05-01,42
321 | 2005-06-01,42
322 | 2005-07-01,42
323 | 2005-08-01,42
324 | 2005-09-01,42
325 | 2005-10-01,42
326 | 2005-11-01,42
327 | 2005-12-01,42
328 | 2006-01-01,42
329 | 2006-02-01,42
330 | 2006-03-01,42
331 | 2006-04-01,42
332 | 2006-05-01,42
333 | 2006-06-01,42
334 | 2006-07-01,42
335 | 2006-08-01,42
336 | 2006-09-01,42
337 | 2006-10-01,42
338 | 2006-11-01,42
339 | 2006-12-01,42
340 | 2007-01-01,42
341 | 2007-02-01,42
342 | 2007-03-01,42
343 | 2007-04-01,42
344 | 2007-05-01,42
345 | 2007-06-01,42
346 | 2007-07-01,42
347 | 2007-08-01,42
348 | 2007-09-01,42
349 | 2007-10-01,42
350 | 2007-11-01,42
351 | 2007-12-01,42
352 | 2008-01-01,42
353 | 2008-02-01,42
354 | 2008-03-01,42
355 | 2008-04-01,42
356 | 2008-05-01,42
357 | 2008-06-01,42
358 | 2008-07-01,42
359 | 2008-08-01,42
360 | 2008-09-01,42
361 | 2008-10-01,42
362 | 2008-11-01,42
363 | 2008-12-01,42
364 | 2009-01-01,42
365 | 2009-02-01,42
366 | 2009-03-01,42
367 | 2009-04-01,42
368 | 2009-05-01,42
369 | 2009-06-01,42
370 | 2009-07-01,42
371 | 2009-08-01,42
372 | 2009-09-01,42
373 | 2009-10-01,42
374 | 2009-11-01,42
375 | 2009-12-01,42
376 | 2010-01-01,42
377 | 2010-02-01,42
378 | 2010-03-01,42
379 | 2010-04-01,42
380 | 2010-05-01,42
381 | 2010-06-01,42
382 | 2010-07-01,42
383 | 2010-08-01,42
384 | 2010-09-01,42
385 | 2010-10-01,42
386 | 2010-11-01,42
387 | 2010-12-01,42
388 | 2011-01-01,42
389 | 2011-02-01,42
390 | 2011-03-01,42
391 | 2011-04-01,42
392 | 2011-05-01,42
393 | 2011-06-01,42
394 | 2011-07-01,42
395 | 2011-08-01,42
396 | 2011-09-01,42
397 | 2011-10-01,42
398 | 2011-11-01,42
399 | 2011-12-01,42
400 | 2012-01-01,42
401 | 2012-02-01,42
402 | 2012-03-01,42
403 | 2012-04-01,42
404 | 2012-05-01,42
405 | 2012-06-01,42
406 | 2012-07-01,42
407 | 2012-08-01,42
408 | 2012-09-01,42
409 | 2012-10-01,42
410 | 2012-11-01,42
411 | 2012-12-01,42
412 | 2013-01-01,42
413 | 2013-02-01,42
414 | 2013-03-01,42
415 | 2013-04-01,42
416 | 2013-05-01,42
417 | 2013-06-01,42
418 | 2013-07-01,42
419 | 2013-08-01,42
420 | 2013-09-01,42
421 | 2013-10-01,42
422 | 2013-11-01,42
423 | 2013-12-01,42
424 | 2014-01-01,42
425 | 2014-02-01,42
426 | 2014-03-01,42
427 | 2014-04-01,42
428 | 2014-05-01,42
429 | 2014-06-01,42
430 | 2014-07-01,42
431 | 2014-08-01,42
432 | 2014-09-01,42
433 | 2014-10-01,42
434 | 2014-11-01,42
435 | 2014-12-01,42
436 | 2015-01-01,42
437 | 2015-02-01,42
438 | 2015-03-01,42
439 | 2015-04-01,42
440 | 2015-05-01,42
441 | 2015-06-01,42
442 | 2015-07-01,42
443 | 2015-08-01,42
444 | 2015-09-01,42
445 | 2015-10-01,42
446 | 2015-11-01,42
447 | 2015-12-01,42
448 | 2016-01-01,42
449 | 2016-02-01,42
450 | 2016-03-01,42
451 | 2016-04-01,42
452 | 2016-05-01,42
453 | 2016-06-01,42
454 | 2016-07-01,42
455 | 2016-08-01,42
456 | 2016-09-01,42
457 | 2016-10-01,42
458 | 2016-11-01,42
459 | 2016-12-01,42
460 | 2017-01-01,42
461 | 2017-02-01,42
462 | 2017-03-01,42
463 | 2017-04-01,42
464 | 2017-05-01,42
465 | 2017-06-01,42
466 | 2017-07-01,42
467 | 2017-08-01,42
468 | 2017-09-01,42
469 | 2017-10-01,42
470 | 2017-11-01,42
471 | 2017-12-01,42
472 | 2018-01-01,42
473 | 2018-02-01,42
474 | 2018-03-01,42
475 | 2018-04-01,42
476 | 2018-05-01,42
477 | 2018-06-01,42
478 | 2018-07-01,42
479 | 2018-08-01,42
480 | 2018-09-01,42
481 | 2018-10-01,42
482 | 2018-11-01,42
483 | 2018-12-01,42
484 | 2019-01-01,42
485 | 2019-02-01,42
486 | 2019-03-01,42
487 | 2019-04-01,42
488 | 2019-05-01,42
489 | 2019-06-01,42
490 | 2019-07-01,42
491 | 2019-08-01,42
492 | 2019-09-01,42
493 | 2019-10-01,42
494 | 2019-11-01,42
495 | 2019-12-01,42
496 | 2020-01-01,42
497 | 2020-02-01,42
498 | 2020-03-01,42
499 | 2020-04-01,42
500 | 2020-05-01,42
501 | 2020-06-01,42
502 | 2020-07-01,42
503 | 2020-08-01,42
504 | 2020-09-01,42
505 | 2020-10-01,42
506 | 2020-11-01,42
507 | 2020-12-01,42
508 | 2021-01-01,42
509 | 2021-02-01,42
510 | 2021-03-01,42
511 | 2021-04-01,42
512 | 2021-05-01,42
513 | 2021-06-01,42
514 | 2021-07-01,42
515 | 2021-08-01,42
516 | 2021-09-01,42
517 | 2021-10-01,42
518 | 2021-11-01,42
519 | 2021-12-01,42
520 | 2022-01-01,42
521 | 2022-02-01,42
522 | 2022-03-01,42
523 | 2022-04-01,42
524 | 2022-05-01,42
525 | 2022-06-01,42
526 | 2022-07-01,42
527 | 2022-08-01,42
528 | 2022-09-01,42
529 | 2022-10-01,42
530 | 2022-11-01,42
531 | 2022-12-01,42
532 | 2023-01-01,42
533 | 


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<MonthBegin>_AirlinePassengers.csv:
--------------------------------------------------------------------------------
  1 | Month,Passengers
  2 | 1980-01,42
  3 | 1980-02,42
  4 | 1980-03,42
  5 | 1980-04,42
  6 | 1980-05,42
  7 | 1980-06,42
  8 | 1980-07,42
  9 | 1980-08,42
 10 | 1980-09,42
 11 | 1980-10,42
 12 | 1980-11,42
 13 | 1980-12,42
 14 | 1981-01,42
 15 | 1981-02,42
 16 | 1981-03,42
 17 | 1981-04,42
 18 | 1981-05,42
 19 | 1981-06,42
 20 | 1981-07,42
 21 | 1981-08,42
 22 | 1981-09,42
 23 | 1981-10,42
 24 | 1981-11,42
 25 | 1981-12,42
 26 | 1982-01,42
 27 | 1982-02,42
 28 | 1982-03,42
 29 | 1982-04,42
 30 | 1982-05,42
 31 | 1982-06,42
 32 | 1982-07,42
 33 | 1982-08,42
 34 | 1982-09,42
 35 | 1982-10,42
 36 | 1982-11,42
 37 | 1982-12,42
 38 | 1983-01,42
 39 | 1983-02,42
 40 | 1983-03,42
 41 | 1983-04,42
 42 | 1983-05,42
 43 | 1983-06,42
 44 | 1983-07,42
 45 | 1983-08,42
 46 | 1983-09,42
 47 | 1983-10,42
 48 | 1983-11,42
 49 | 1983-12,42
 50 | 1984-01,42
 51 | 1984-02,42
 52 | 1984-03,42
 53 | 1984-04,42
 54 | 1984-05,42
 55 | 1984-06,42
 56 | 1984-07,42
 57 | 1984-08,42
 58 | 1984-09,42
 59 | 1984-10,42
 60 | 1984-11,42
 61 | 1984-12,42
 62 | 1985-01,42
 63 | 1985-02,42
 64 | 1985-03,42
 65 | 1985-04,42
 66 | 1985-05,42
 67 | 1985-06,42
 68 | 1985-07,42
 69 | 1985-08,42
 70 | 1985-09,42
 71 | 1985-10,42
 72 | 1985-11,42
 73 | 1985-12,42
 74 | 1986-01,42
 75 | 1986-02,42
 76 | 1986-03,42
 77 | 1986-04,42
 78 | 1986-05,42
 79 | 1986-06,42
 80 | 1986-07,42
 81 | 1986-08,42
 82 | 1986-09,42
 83 | 1986-10,42
 84 | 1986-11,42
 85 | 1986-12,42
 86 | 1987-01,42
 87 | 1987-02,42
 88 | 1987-03,42
 89 | 1987-04,42
 90 | 1987-05,42
 91 | 1987-06,42
 92 | 1987-07,42
 93 | 1987-08,42
 94 | 1987-09,42
 95 | 1987-10,42
 96 | 1987-11,42
 97 | 1987-12,42
 98 | 1988-01,42
 99 | 1988-02,42
100 | 1988-03,42
101 | 1988-04,42
102 | 1988-05,42
103 | 1988-06,42
104 | 1988-07,42
105 | 1988-08,42
106 | 1988-09,42
107 | 1988-10,42
108 | 1988-11,42
109 | 1988-12,42
110 | 1989-01,42
111 | 1989-02,42
112 | 1989-03,42
113 | 1989-04,42
114 | 1989-05,42
115 | 1989-06,42
116 | 1989-07,42
117 | 1989-08,42
118 | 1989-09,42
119 | 1989-10,42
120 | 1989-11,42
121 | 1989-12,42
122 | 1990-01,42
123 | 1990-02,42
124 | 1990-03,42
125 | 1990-04,42
126 | 1990-05,42
127 | 1990-06,42
128 | 1990-07,42
129 | 1990-08,42
130 | 1990-09,42
131 | 1990-10,42
132 | 1990-11,42
133 | 1990-12,42
134 | 


--------------------------------------------------------------------------------
/tests/test_datasets/infer_freq_datasets/<Week: weekday=4>_StockExample.csv:
--------------------------------------------------------------------------------
  1 | Data,Ultimo
  2 | 2021-01-08,42
  3 | 2021-01-15,42
  4 | 2021-01-22,42
  5 | 2021-01-29,42
  6 | 2021-02-05,42
  7 | 2021-02-12,42
  8 | 2021-02-19,42
  9 | 2021-02-26,42
 10 | 2021-03-05,42
 11 | 2021-03-12,42
 12 | 2021-03-19,42
 13 | 2021-03-26,42
 14 | 2021-04-09,42
 15 | 2021-04-16,42
 16 | 2021-04-23,42
 17 | 2021-04-30,42
 18 | 2021-05-07,42
 19 | 2021-05-14,42
 20 | 2021-05-21,42
 21 | 2021-05-28,42
 22 | 2021-06-04,42
 23 | 2021-06-11,42
 24 | 2021-06-18,42
 25 | 2021-06-25,42
 26 | 2021-07-02,42
 27 | 2021-07-09,42
 28 | 2021-07-16,42
 29 | 2021-07-23,42
 30 | 2021-07-30,42
 31 | 2021-08-06,42
 32 | 2021-08-13,42
 33 | 2021-08-20,42
 34 | 2021-08-27,42
 35 | 2021-09-03,42
 36 | 2021-09-10,42
 37 | 2021-09-17,42
 38 | 2021-09-24,42
 39 | 2021-10-01,42
 40 | 2021-10-08,42
 41 | 2021-10-15,42
 42 | 2021-10-22,42
 43 | 2021-10-29,42
 44 | 2021-11-05,42
 45 | 2021-11-12,42
 46 | 2021-11-19,42
 47 | 2021-11-26,42
 48 | 2021-12-03,42
 49 | 2021-12-10,42
 50 | 2021-12-17,42
 51 | 2022-01-07,42
 52 | 2022-01-14,42
 53 | 2022-01-21,42
 54 | 2022-01-28,42
 55 | 2022-02-04,42
 56 | 2022-02-11,42
 57 | 2022-02-18,42
 58 | 2022-02-25,42
 59 | 2022-03-04,42
 60 | 2022-03-11,42
 61 | 2022-03-18,42
 62 | 2022-03-25,42
 63 | 2022-04-01,42
 64 | 2022-04-08,42
 65 | 2022-04-22,42
 66 | 2022-04-29,42
 67 | 2022-05-06,42
 68 | 2022-05-13,42
 69 | 2022-05-20,42
 70 | 2022-05-27,42
 71 | 2022-06-03,42
 72 | 2022-06-10,42
 73 | 2022-06-17,42
 74 | 2022-06-24,42
 75 | 2022-07-01,42
 76 | 2022-07-08,42
 77 | 2022-07-15,42
 78 | 2022-07-22,42
 79 | 2022-07-29,42
 80 | 2022-08-05,42
 81 | 2022-08-12,42
 82 | 2022-08-19,42
 83 | 2022-08-26,42
 84 | 2022-09-02,42
 85 | 2022-09-09,42
 86 | 2022-09-16,42
 87 | 2022-09-23,42
 88 | 2022-09-30,42
 89 | 2022-10-07,42
 90 | 2022-10-14,42
 91 | 2022-10-21,42
 92 | 2022-10-28,42
 93 | 2022-11-04,42
 94 | 2022-11-11,42
 95 | 2022-11-18,42
 96 | 2022-11-25,42
 97 | 2022-12-02,42
 98 | 2022-12-09,42
 99 | 2022-12-16,42
100 | 2022-12-23,42
101 | 2022-12-30,42
102 | 2023-01-06,42
103 | 2023-01-13,42
104 | 2023-01-20,42
105 | 2023-01-27,42
106 | 2023-02-03,42
107 | 2023-02-10,42
108 | 2023-02-17,42
109 | 2023-02-24,42
110 | 2023-03-03,42
111 | 2023-03-10,42
112 | 2023-03-17,42
113 | 2023-03-24,42
114 | 2023-03-31,42
115 | 2023-04-14,42
116 | 2023-04-21,42
117 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_1.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 2020-02-25T00:00:00,2,3
3 | 2020-02-26T00:00:00,5,6
4 | 2020-02-27T00:00:00,8,9
5 | 
6 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_1_1.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 2011-01-31T18:00:00,2,3
3 | 2011-02-28T18:00:00,5,6
4 | 2011-03-31T18:00:00,8,9
5 | 
6 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_1_2.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 2020-02-25T00:00:00,2,3
3 | 2020-02-26T00:00:00,5,6
4 | 2020-02-27T00:00:00,8,9
5 | 2020-02-28T00:00:00,11,12
6 | 
7 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_2.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 2020-02-25T18:00:00,2,3
3 | 2020-02-26T18:00:00,5,8
4 | 2020-02-27T18:00:00,8,15
5 | 
6 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_3.csv:
--------------------------------------------------------------------------------
 1 | first_column,second_column
 2 | 2020-02-25T18:00:00,8
 3 | 2020-02-26T18:00:00,8
 4 | 2020-02-27T18:00:00,8
 5 | 2020-02-28T18:00:00,8
 6 | 2020-03-01T18:00:00,8
 7 | 2020-03-02T18:00:00,8
 8 | 2020-03-03T18:00:00,8
 9 | 2020-03-04T18:00:00,8
10 | 2020-03-05T18:00:00,8
11 | 2020-03-06T18:00:00,8
12 | 
13 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_4.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column
2 | 2020-02-25T18:00:00,1
3 | 2020-02-26T18:00:00,2
4 | 2020-02-26T18:00:00,3
5 | 2020-02-27T18:00:00,4
6 | 
7 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_5.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | Nov. 2020,2,3
3 | Dic. 2020,5,6
4 | Gen. 2021,8,9
5 | 
6 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_5_1.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 1959-01,2,3
3 | 1959-02,5,6
4 | 1959-03,8,9
5 | 
6 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_6.csv:
--------------------------------------------------------------------------------
 1 | first_column,second_column
 2 | 2020-02-25T12:30:14,1
 3 | 2020-02-26T12:30:12,1
 4 | 2020-02-27T11:22:11,2
 5 | 2020-02-28T12:22:12,3
 6 | 2020-03-19T11:24:13,4
 7 | 2020-03-20T10:22:13,4
 8 | 2020-03-21T11:22:13,4
 9 | 2020-04-27T11:22:15,5
10 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_6_expected.csv:
--------------------------------------------------------------------------------
 1 | first_column,second_column
 2 | 2020-02-25,1.0
 3 | 2020-02-26,1.0
 4 | 2020-02-27,2.0
 5 | 2020-02-28,3.0
 6 | 2020-02-29,3.05
 7 | 2020-03-01,3.1
 8 | 2020-03-02,3.15
 9 | 2020-03-03,3.2
10 | 2020-03-04,3.25
11 | 2020-03-05,3.3
12 | 2020-03-06,3.35
13 | 2020-03-07,3.4
14 | 2020-03-08,3.45
15 | 2020-03-09,3.5
16 | 2020-03-10,3.55
17 | 2020-03-11,3.6
18 | 2020-03-12,3.65
19 | 2020-03-13,3.7
20 | 2020-03-14,3.75
21 | 2020-03-15,3.8
22 | 2020-03-16,3.85
23 | 2020-03-17,3.9
24 | 2020-03-18,3.95
25 | 2020-03-19,4.0
26 | 2020-03-20,4.0
27 | 2020-03-21,4.0
28 | 2020-03-22,4.027027027027027
29 | 2020-03-23,4.054054054054054
30 | 2020-03-24,4.081081081081081
31 | 2020-03-25,4.108108108108108
32 | 2020-03-26,4.135135135135135
33 | 2020-03-27,4.162162162162162
34 | 2020-03-28,4.1891891891891895
35 | 2020-03-29,4.216216216216216
36 | 2020-03-30,4.243243243243243
37 | 2020-03-31,4.27027027027027
38 | 2020-04-01,4.297297297297297
39 | 2020-04-02,4.324324324324325
40 | 2020-04-03,4.351351351351352
41 | 2020-04-04,4.378378378378378
42 | 2020-04-05,4.405405405405405
43 | 2020-04-06,4.4324324324324325
44 | 2020-04-07,4.45945945945946
45 | 2020-04-08,4.486486486486487
46 | 2020-04-09,4.513513513513514
47 | 2020-04-10,4.54054054054054
48 | 2020-04-11,4.5675675675675675
49 | 2020-04-12,4.594594594594595
50 | 2020-04-13,4.621621621621622
51 | 2020-04-14,4.648648648648649
52 | 2020-04-15,4.675675675675675
53 | 2020-04-16,4.702702702702703
54 | 2020-04-17,4.72972972972973
55 | 2020-04-18,4.756756756756757
56 | 2020-04-19,4.783783783783784
57 | 2020-04-20,4.8108108108108105
58 | 2020-04-21,4.837837837837838
59 | 2020-04-22,4.864864864864865
60 | 2020-04-23,4.891891891891892
61 | 2020-04-24,4.918918918918919
62 | 2020-04-25,4.945945945945946
63 | 2020-04-26,4.972972972972973
64 | 2020-04-27,5.0
65 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_7.csv:
--------------------------------------------------------------------------------
  1 | date,a,b
  2 | 2000-01-01,1,101
  3 | 2000-01-02,2,102
  4 | 2000-01-03,3,103
  5 | 2000-01-04,4,104
  6 | 2000-01-05,5,105
  7 | 2000-01-06,6,106
  8 | 2000-01-07,7,107
  9 | 2000-01-08,8,108
 10 | 2000-01-09,9,109
 11 | 2000-01-10,10,110
 12 | 2000-01-11,11,111
 13 | 2000-01-12,12,112
 14 | 2000-01-13,13,113
 15 | 2000-01-14,14,114
 16 | 2000-01-15,15,115
 17 | 2000-01-16,16,116
 18 | 2000-01-17,17,117
 19 | 2000-01-18,18,118
 20 | 2000-01-19,19,119
 21 | 2000-01-20,20,120
 22 | 2000-01-21,21,121
 23 | 2000-01-22,22,122
 24 | 2000-01-23,23,123
 25 | 2000-01-24,24,124
 26 | 2000-01-25,25,125
 27 | 2000-01-26,26,126
 28 | 2000-01-27,27,127
 29 | 2000-01-28,28,128
 30 | 2000-01-29,29,129
 31 | 2000-01-30,30,130
 32 | 2000-01-31,31,131
 33 | 2000-02-01,32,132
 34 | 2000-02-02,33,133
 35 | 2000-02-03,34,134
 36 | 2000-02-04,35,135
 37 | 2000-02-05,36,136
 38 | 2000-02-06,37,137
 39 | 2000-02-07,38,138
 40 | 2000-02-08,39,139
 41 | 2000-02-09,40,140
 42 | 2000-02-10,41,141
 43 | 2000-02-11,42,142
 44 | 2000-02-12,43,143
 45 | 2000-02-13,44,144
 46 | 2000-02-14,45,145
 47 | 2000-02-15,46,146
 48 | 2000-02-16,47,147
 49 | 2000-02-17,48,148
 50 | 2000-02-18,49,149
 51 | 2000-02-19,50,150
 52 | 2000-02-20,51,151
 53 | 2000-02-21,52,152
 54 | 2000-02-22,53,153
 55 | 2000-02-23,54,154
 56 | 2000-02-24,55,155
 57 | 2000-02-25,56,156
 58 | 2000-02-26,57,157
 59 | 2000-02-27,58,158
 60 | 2000-02-28,59,159
 61 | 2000-02-29,60,160
 62 | 2000-03-01,61,161
 63 | 2000-03-02,62,162
 64 | 2000-03-03,63,163
 65 | 2000-03-04,64,164
 66 | 2000-03-05,65,165
 67 | 2000-03-06,66,166
 68 | 2000-03-07,67,167
 69 | 2000-03-08,68,168
 70 | 2000-03-09,69,169
 71 | 2000-03-10,70,170
 72 | 2000-03-11,71,171
 73 | 2000-03-12,72,172
 74 | 2000-03-13,73,173
 75 | 2000-03-14,74,174
 76 | 2000-03-15,75,175
 77 | 2000-03-16,76,176
 78 | 2000-03-17,77,177
 79 | 2000-03-18,78,178
 80 | 2000-03-19,79,179
 81 | 2000-03-20,80,180
 82 | 2000-03-21,81,181
 83 | 2000-03-22,82,182
 84 | 2000-03-23,83,183
 85 | 2000-03-24,84,184
 86 | 2000-03-25,85,185
 87 | 2000-03-26,86,186
 88 | 2000-03-27,87,187
 89 | 2000-03-28,88,188
 90 | 2000-03-29,89,189
 91 | 2000-03-30,90,190
 92 | 2000-03-31,91,191
 93 | 2000-04-01,92,192
 94 | 2000-04-02,93,193
 95 | 2000-04-03,94,194
 96 | 2000-04-04,95,195
 97 | 2000-04-05,96,196
 98 | 2000-04-06,97,197
 99 | 2000-04-07,98,198
100 | 2000-04-08,99,199
101 | 2000-04-09,100,200
102 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_covid.csv:
--------------------------------------------------------------------------------
  1 | data,variazione_totale_positivi,nuovi_positivi
  2 | 2020-08-31,873,996
  3 | 2020-09-01,676,978
  4 | 2020-09-02,1063,1326
  5 | 2020-09-03,1098,1397
  6 | 2020-09-04,1184,1733
  7 | 2020-09-05,1095,1694
  8 | 2020-09-06,884,1297
  9 | 2020-09-07,915,1108
 10 | 2020-09-08,796,1370
 11 | 2020-09-09,945,1434
 12 | 2020-09-10,974,1597
 13 | 2020-09-11,1059,1616
 14 | 2020-09-12,736,1501
 15 | 2020-09-13,1006,1458
 16 | 2020-09-14,678,1008
 17 | 2020-09-15,525,1229
 18 | 2020-09-16,820,1452
 19 | 2020-09-17,881,1585
 20 | 2020-09-18,1044,1907
 21 | 2020-09-19,704,1638
 22 | 2020-09-20,937,1587
 23 | 2020-09-21,981,1350
 24 | 2020-09-22,410,1392
 25 | 2020-09-23,625,1640
 26 | 2020-09-24,666,1786
 27 | 2020-09-25,938,1912
 28 | 2020-09-26,875,1869
 29 | 2020-09-27,1025,1766
 30 | 2020-09-28,705,1494
 31 | 2020-09-29,307,1648
 32 | 2020-09-30,633,1851
 33 | 2020-10-01,1384,2548
 34 | 2020-10-02,1350,2499
 35 | 2020-10-03,1569,2844
 36 | 2020-10-04,1863,2578
 37 | 2020-10-05,1474,2257
 38 | 2020-10-06,1231,2677
 39 | 2020-10-07,2442,3678
 40 | 2020-10-08,3376,4458
 41 | 2020-10-09,4158,5372
 42 | 2020-10-10,4719,5724
 43 | 2020-10-11,4246,5456
 44 | 2020-10-12,3689,4619
 45 | 2020-10-13,4429,5901
 46 | 2020-10-14,5252,7332
 47 | 2020-10-15,6821,8804
 48 | 2020-10-16,8046,10010
 49 | 2020-10-17,9623,10925
 50 | 2020-10-18,9302,11705
 51 | 2020-10-19,7766,9338
 52 | 2020-10-20,8736,10874
 53 | 2020-10-21,12703,15199
 54 | 2020-10-22,13860,16079
 55 | 2020-10-23,16700,19143
 56 | 2020-10-24,17180,19644
 57 | 2020-10-25,19059,21273
 58 | 2020-10-26,14443,17012
 59 | 2020-10-27,18406,21994
 60 | 2020-10-28,21367,24991
 61 | 2020-10-29,22734,26831
 62 | 2020-10-30,26595,31084
 63 | 2020-10-31,25600,31758
 64 | 2020-11-01,26743,29907
 65 | 2020-11-02,18383,22253
 66 | 2020-11-03,21630,28244
 67 | 2020-11-04,25093,30550
 68 | 2020-11-05,29113,34505
 69 | 2020-11-06,26770,37809
 70 | 2020-11-07,33418,39811
 71 | 2020-11-08,26100,32616
 72 | 2020-11-09,14698,25271
 73 | 2020-11-10,16776,35098
 74 | 2020-11-11,23248,32961
 75 | 2020-11-12,21696,37978
 76 | 2020-11-13,28872,40902
 77 | 2020-11-14,24509,37255
 78 | 2020-11-15,24055,33979
 79 | 2020-11-16,5294,27354
 80 | 2020-11-17,16026,32191
 81 | 2020-11-18,9358,34282
 82 | 2020-11-19,18503,36176
 83 | 2020-11-20,15505,37242
 84 | 2020-11-21,14570,34767
 85 | 2020-11-22,14201,28337
 86 | 2020-11-23,-9098,22930
 87 | 2020-11-24,1537,23232
 88 | 2020-11-25,-6689,25853
 89 | 2020-11-26,4148,29003
 90 | 2020-11-27,-7952,28352
 91 | 2020-11-28,1415,26323
 92 | 2020-11-29,6463,20648
 93 | 2020-11-30,-7300,16377
 94 | 2020-12-01,-8526,19350
 95 | 2020-12-02,-18715,20709
 96 | 2020-12-03,-1248,23225
 97 | 2020-12-04,-2280,24099
 98 | 2020-12-05,-3533,21052
 99 | 2020-12-06,1137,18887
100 | 2020-12-07,-6487,13720
101 | 2020-12-08,-11294,14842
102 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_create_containers_extrareg_d.csv:
--------------------------------------------------------------------------------
 1 | date,d
 2 | 2000-01-01,3
 3 | 2000-01-02,3
 4 | 2000-01-03,3
 5 | 2000-01-04,3
 6 | 2000-01-05,3
 7 | 2000-01-06,3
 8 | 2000-01-07,3
 9 | 2000-01-08,3
10 | 2000-01-09,3
11 | 2000-01-10,3
12 | 2000-01-11,3
13 | 2000-01-12,3
14 | 2000-01-13,3
15 | 2000-01-14,3
16 | 2000-01-15,3
17 | 2000-01-16,3
18 | 2000-01-17,3
19 | 2000-01-18,3
20 | 2000-01-19,3
21 | 2000-01-20,3
22 | 2000-01-21,3
23 | 2000-01-22,3
24 | 2000-01-23,3
25 | 2000-01-24,3
26 | 2000-01-25,3
27 | 2000-01-26,3
28 | 2000-01-27,3
29 | 2000-01-28,3
30 | 2000-01-29,3
31 | 2000-01-30,3
32 | 2000-01-31,3
33 | 2000-02-01,3
34 | 2000-02-02,3
35 | 2000-02-03,3
36 | 2000-02-04,3
37 | 2000-02-05,3
38 | 2000-02-06,3
39 | 2000-02-07,3
40 | 2000-02-08,3
41 | 2000-02-09,3
42 | 2000-02-10,3
43 | 2000-02-11,3
44 | 2000-02-12,3
45 | 2000-02-13,3
46 | 2000-02-14,3
47 | 2000-02-15,3
48 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_create_containers_extrareg_e.csv:
--------------------------------------------------------------------------------
 1 | date,e
 2 | 2000-01-01,3
 3 | 2000-01-02,3
 4 | 2000-01-03,3
 5 | 2000-01-04,3
 6 | 2000-01-05,3
 7 | 2000-01-06,3
 8 | 2000-01-07,3
 9 | 2000-01-08,3
10 | 2000-01-09,3
11 | 2000-01-10,3
12 | 2000-01-11,3
13 | 2000-01-12,3
14 | 2000-01-13,3
15 | 2000-01-14,3
16 | 2000-01-15,3
17 | 2000-01-16,3
18 | 2000-01-17,3
19 | 2000-01-18,3
20 | 2000-01-19,3
21 | 2000-01-20,3
22 | 2000-01-21,3
23 | 2000-01-22,3
24 | 2000-01-23,3
25 | 2000-01-24,3
26 | 2000-01-25,3
27 | 2000-01-26,3
28 | 2000-01-27,3
29 | 2000-01-28,3
30 | 2000-01-29,3
31 | 2000-01-30,3
32 | 2000-01-31,3
33 | 2000-02-01,3
34 | 2000-02-02,3
35 | 2000-02-03,3
36 | 2000-02-04,3
37 | 2000-02-05,3
38 | 2000-02-06,3
39 | 2000-02-07,3
40 | 2000-02-08,3
41 | 2000-02-09,3
42 | 2000-02-10,3
43 | 2000-02-11,3
44 | 2000-02-12,3
45 | 2000-02-13,3
46 | 2000-02-14,3
47 | 2000-02-15,3
48 | 


--------------------------------------------------------------------------------
/tests/test_datasets/test_data_reordering.csv:
--------------------------------------------------------------------------------
1 | first_column,second_column,third_column
2 | 2020-02-28,11,12
3 | 2020-02-25,2,3
4 | 2020-02-27,8,9
5 | 2020-02-26,5,6
6 | 
7 | 


--------------------------------------------------------------------------------
/tests/utilities.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import pandas
 3 | from pandas import DataFrame
 4 | 
 5 | 
 6 | def get_fake_df(length: int, features: int = 1, name: str = "value") -> DataFrame:
 7 |     dates = pandas.date_range('1/1/2000', periods=length)
 8 | 
 9 |     np.random.seed(0)
10 |     df = pandas.DataFrame({f"{name}_{i}": np.random.randn(length) for i in range(0, features)},
11 |                           index=dates)
12 |     return df
13 | 
14 | 


--------------------------------------------------------------------------------
/timexseries/__init__.py:
--------------------------------------------------------------------------------
  1 | """
  2 | .. include:: ./documentation.md
  3 | """
  4 | from math import sqrt
  5 | 
  6 | import numpy as np
  7 | import pandas as pd
  8 | 
  9 | from pandas import DataFrame, Series
 10 | 
 11 | 
 12 | class TimeSeriesContainer:
 13 |     """
 14 |     A TimeSeriesContainer collect all the relevant information useful to characterize a single time-series coming from
 15 |     the ingested dataset.
 16 | 
 17 |     Parameters
 18 |     ----------
 19 |     timeseries_data : DataFrame
 20 |         Historical time-series data, in the form of a DataFrame with a index and a single data column.
 21 |     models : dict
 22 |         Dictionary of ModelResult objects, all trained on this time-series.
 23 |     xcorr : dict
 24 |         Cross-correlation between the data of this time-series and all the other ones.
 25 |     historical_prediction : dict
 26 |         The historical prediction, i.e. the predictions computed on a rolling window on the historical data.
 27 |         This is useful to verify the performances of each model not only on the very last data, but throughout the
 28 |         history of the time-series, in a cross-validation fashion. This dictionary contains one entry for each model
 29 |         tested, and each of this entry contains two keys: 'series', i.e., the actual historical prediction, and
 30 |         'metrics', i.e. validation performances of the historical predictions on real data.
 31 |     """
 32 |     def __init__(self, timeseries_data: DataFrame, models: dict, xcorr: dict, historical_prediction: dict = None):
 33 |         self.timeseries_data = timeseries_data
 34 |         self.models = models
 35 |         self.xcorr = xcorr
 36 |         self.historical_prediction = historical_prediction
 37 | 
 38 |     def set_historical_prediction(self, historical_prediction):
 39 |         self.historical_prediction = historical_prediction
 40 | 
 41 | 
 42 | class ValidationPerformance:
 43 |     """
 44 |     Class for the summary of various statistical indexes relative to the performance of a prediction model.
 45 | 
 46 |     Parameters
 47 |     ----------
 48 |     first_used_index, optional, default None
 49 |         Index of the first value used in the time series to generate these results. This is a convenience if you already
 50 |         know that initial index of the data this performance will refer to.
 51 | 
 52 |     Attributes
 53 |     ----------
 54 |     MSE : float
 55 |         Mean Squared Error. Default 0
 56 |     RMSE: float
 57 |         Root Mean Squared Error. Default 0
 58 |     MAE: float
 59 |         Mean Absolute Error. Default 0
 60 |     AM: float
 61 |         Arithmetic Mean of error. Default 0
 62 |     SD: float
 63 |         Standard deviation of error. Default 0
 64 |     """
 65 |     def __init__(self, first_used_index=None):
 66 |         self.first_used_index = first_used_index
 67 |         self.MSE = 0
 68 |         self.RMSE = 0
 69 |         self.MAE = 0
 70 |         self.AM = 0
 71 |         self.SD = 0
 72 | 
 73 |     def set_testing_stats(self, actual: Series, predicted: Series):
 74 |         """
 75 |         Set all the statistical indexes according to input data.
 76 | 
 77 |         Parameters
 78 |         ----------
 79 |         actual : Series
 80 |             Actual data stored in a Pandas Series.
 81 |         predicted : Series
 82 |             Data predicted by a model, stored in a Pandas Series.
 83 | 
 84 |         Examples
 85 |         --------
 86 |         >>> dates = pd.date_range('2000-01-01', periods=5)
 87 |         >>> ds = pd.DatetimeIndex(dates, freq="D")
 88 |         >>> actual = np.array([1, 1, 1, 1, 1])
 89 |         >>> predicted = np.array([3, 3, 3, 3, 3])
 90 |         >>> actual_dataframe = DataFrame(data={"a": actual}, index=ds)
 91 |         >>> predicted_dataframe = DataFrame(data={"yhat": predicted}, index=ds)
 92 | 
 93 |         Calculate the performances.
 94 |         >>> perf = ValidationPerformance()
 95 |         >>> perf.set_testing_stats(actual_dataframe['a'], predicted_dataframe['yhat'])
 96 | 
 97 |         >>> print(perf.MAE)
 98 |         2.0
 99 | 
100 |         >>> print(perf.MSE)
101 |         4.0
102 |         """
103 |         self.MSE = np.square(np.subtract(actual,predicted)).mean()
104 |         self.MAE = np.abs(np.subtract(actual, predicted)).mean()
105 | 
106 |         self.RMSE = sqrt(self.MSE)
107 |         self.AM = sum([y - yhat for y, yhat in zip(actual, predicted)]) / len(actual)
108 |         self.SD = (actual - predicted).std(ddof=0)
109 | 
110 |     def get_dict(self) -> dict:
111 |         """
112 |         Return all the parameters, in a dict.
113 | 
114 |         Returns
115 |         -------
116 |         d : dict
117 |             All the statistics, in a dict.
118 | 
119 |         Examples
120 |         --------
121 |         >>> perf = ValidationPerformance()
122 |         >>> perf.set_testing_stats(actual_dataframe['a'], predicted_dataframe['yhat'])
123 |         >>> perf.get_dict()
124 |         {'first_used_index': None, 'MSE': 4.0, 'RMSE': 2.0, 'MAE': 2.0, 'AM': -2.0, 'SD': 0.0}
125 |         """
126 |         d = {}
127 |         for attribute, value in self.__dict__.items():
128 |             d[attribute] = value
129 | 
130 |         return d
131 | 
132 | 
133 | class SingleResult:
134 |     """
135 |     Class for the result of a model, trained on a specific training set.
136 | 
137 |     Parameters
138 |     ----------
139 |     prediction : DataFrame
140 |         Estimated prediction, using this training set
141 |     testing_performances : ValidationPerformance
142 |         Testing performance (`timexseries.data_prediction.validation_performances.ValidationPerformance`), on the validation
143 |         set, obtained using this training set to train the model.
144 |     """
145 | 
146 |     def __init__(self, prediction: DataFrame, testing_performances: ValidationPerformance):
147 |         self.prediction = prediction
148 |         self.testing_performances = testing_performances
149 | 
150 | 
151 | class ModelResult:
152 |     """
153 |     Class for to collect the global results of a model trained on a time-series.
154 | 
155 |     Parameters
156 |     ----------
157 |     results : [SingleResult]
158 |         List of all the results obtained using all the possible training set for this model, on the time series.
159 |         This is useful to create plots which show how the performance vary changing the training data (e.g.
160 |         `timexseries.data_visualization.functions.performance_plot`).
161 |     characteristics : dict
162 |         Model parameters. This dictionary collects human-readable characteristics of the model, e.g. the used number of
163 |         validation points used, the length of the sliding training window, etc.
164 |     best_prediction : DataFrame
165 |         Prediction obtained using the best training window and _all_ the available points in the time-series. This is
166 |         the prediction that users are most likely to want.
167 |     """
168 | 
169 |     def __init__(self, results: [SingleResult], characteristics: dict, best_prediction: DataFrame):
170 |         self.results = results
171 |         self.characteristics = characteristics
172 |         self.best_prediction = best_prediction
173 | 
174 | 
175 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/__init__.py:
--------------------------------------------------------------------------------
1 | from .. import ValidationPerformance
2 | from .models.predictor import PredictionModel
3 | from .pipeline import create_timeseries_containers, get_result_dict


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/DhiriaDev/TIMEX/767841a830bdc6fafbe1702700b9f80175e357de/timexseries/data_prediction/models/__init__.py


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/arima.py:
--------------------------------------------------------------------------------
 1 | import warnings
 2 | 
 3 | from pandas import DataFrame
 4 | 
 5 | from statsforecast import StatsForecast
 6 | from timexseries.data_prediction import PredictionModel
 7 | from statsforecast.models import AutoARIMA
 8 | 
 9 | from timexseries.data_prediction.models.seasonality_estimator import estimate_seasonality
10 | 
11 | 
12 | class ARIMAModel(PredictionModel):
13 |     """ARIMA prediction model."""
14 |     def __init__(self, params: dict, transformation: str = "nonconda e"):
15 |         super().__init__(params, name="ARIMA", transformation=transformation)
16 | 
17 |     def predict(self, train_data: DataFrame, points_to_predict: int,
18 |                 future_dataframe: DataFrame, extra_regressor: DataFrame = None) -> DataFrame:
19 |         freq = train_data.index.freq
20 | 
21 |         seasonality = estimate_seasonality(train_data)
22 | 
23 |         train_data.reset_index(inplace=True)
24 |         train_data.columns = ['ds', 'y']
25 |         train_data.loc[:, 'unique_id'] = 0
26 | 
27 |         model = StatsForecast(
28 |             df=train_data,
29 |             models=[AutoARIMA(approximation=True, season_length=seasonality,
30 |                               seasonal=seasonality != 1, truncate=3*seasonality if seasonality != 1 else None)],
31 |             freq=freq
32 |         )
33 | 
34 |         with warnings.catch_warnings():
35 |             warnings.simplefilter("ignore")
36 |             y_hat_df = model.forecast(points_to_predict).set_index("ds")
37 | 
38 |         future_dataframe.iloc[-points_to_predict:, 0] = y_hat_df.loc[:, 'AutoARIMA']
39 | 
40 |         return future_dataframe
41 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/exponential_smoothing.py:
--------------------------------------------------------------------------------
 1 | import logging
 2 | 
 3 | from pandas import DataFrame
 4 | from statsforecast import StatsForecast
 5 | from statsforecast.models import AutoETS
 6 | 
 7 | from timexseries.data_prediction import PredictionModel
 8 | from timexseries.data_prediction.models.seasonality_estimator import estimate_seasonality
 9 | 
10 | log = logging.getLogger(__name__)
11 | 
12 | 
13 | class ExponentialSmoothingModel(PredictionModel):
14 |     """
15 |     Exponential smoothing prediction model.
16 |     This model uses the Holt Winters method and automatic parameters estimation.
17 |     """
18 | 
19 |     def __init__(self, params: dict, transformation: str = "none"):
20 |         super().__init__(params, name="ExponentialSmoothing", transformation=transformation)
21 | 
22 |     def predict(self, train_data: DataFrame, points_to_predict: int, 
23 |                 future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
24 |         """Overrides PredictionModel.predict()"""
25 |         freq = train_data.index.freq
26 | 
27 |         seasonality = estimate_seasonality(train_data)
28 | 
29 |         seasonality = min(24, seasonality)
30 | 
31 |         train_data.reset_index(inplace=True)
32 |         train_data.columns = ['ds', 'y']
33 |         train_data.loc[:, 'unique_id'] = 0
34 | 
35 |         # if seasonality == 1:
36 |         #     model = 'ZZN'
37 |         # else:
38 |         #     model = 'ZZA'
39 | 
40 |         model = StatsForecast(
41 |             df=train_data,
42 |             models=[AutoETS(season_length=seasonality, model='ZZZ')],
43 |             freq=freq
44 |         )
45 | 
46 |         y_hat_df = model.forecast(points_to_predict, level=[95]).set_index("ds")
47 | 
48 |         future_dataframe.iloc[-points_to_predict:, 0] = y_hat_df.loc[:, 'AutoETS']
49 |         index_to_update = future_dataframe.index[-points_to_predict:]
50 |         future_dataframe.loc[index_to_update, 'yhat_upper'] = y_hat_df.loc[:, 'AutoETS-hi-95']
51 |         future_dataframe.loc[index_to_update, 'yhat_lower'] = y_hat_df.loc[:, 'AutoETS-lo-95']
52 | 
53 |         return future_dataframe
54 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/flaml.py:
--------------------------------------------------------------------------------
 1 | # import logging
 2 | #
 3 | # from pandas import DataFrame
 4 | # import numpy as np
 5 | # from flaml import AutoML
 6 | #
 7 | # from timexseries.data_prediction import PredictionModel
 8 | # log = logging.getLogger(__name__)
 9 | #
10 | #
11 | # class FLAMLModel(PredictionModel):
12 | #     """
13 | #     FLAML
14 | #     """
15 | #
16 | #     def __init__(self, params: dict, transformation: str = "none"):
17 | #         super().__init__(params, name="FLAML", transformation=transformation)
18 | #
19 | #         self.len_train_set = 0
20 | #         self.requested_predictions = 0
21 | #         self.train_data = None
22 | #
23 | #     def train(self, input_data: DataFrame, extra_regressors: DataFrame = None):
24 | #         """Overrides PredictionModel.train()"""
25 | #
26 | #         self.len_train_set = len(input_data)
27 | #         self.train_data = input_data
28 | #
29 | #     def predict(self, future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
30 | #         """Overrides PredictionModel.predict()"""
31 | #         requested_prediction = len(future_dataframe) - self.len_train_set
32 | #         automl = AutoML()
33 | #         settings = {
34 | #             # "time_budget": 10,  # total running time in seconds
35 | #             "metric": 'mae',  # primary metric for validation: 'mape' is generally used for forecast tasks
36 | #             "task": 'ts_forecast',  # task type
37 | #             # "log_file_name": 'CO2_forecast.log',  # flaml log file
38 | #             "eval_method": "auto",  # validation method can be chosen from ['auto', 'holdout', 'cv']
39 | #         }
40 | #         automl.fit(dataframe=self.train_data.reset_index(),  # training data
41 | #                    label=self.train_data.columns[0],  # label column
42 | #                    period=requested_prediction,  # key word argument 'period' must be included for forecast task)
43 | #                    **settings)
44 | #
45 | #         flaml_y_pred = automl.predict(future_dataframe.reset_index().iloc[:, 0].to_frame())
46 | #         future_dataframe.iloc[-requested_prediction:, 0] = flaml_y_pred
47 | #         return future_dataframe
48 | #
49 | #
50 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/linear.py:
--------------------------------------------------------------------------------
 1 | import warnings
 2 | 
 3 | import numpy as np
 4 | from pandas import DataFrame
 5 | from timexseries.data_prediction import PredictionModel
 6 | from sklearn.linear_model import LinearRegression
 7 | 
 8 | 
 9 | class LinearModel(PredictionModel):
10 |     """Linear prediction model."""
11 |     def __init__(self, params: dict, transformation: str = "Linear"):
12 |         super().__init__(params, name="Linear", transformation=transformation)
13 | 
14 |     def predict(self, train_data: DataFrame, points_to_predict: int,
15 |                 future_dataframe: DataFrame, extra_regressor: DataFrame = None) -> DataFrame:
16 | 
17 |         train_data.reset_index(inplace=True)
18 |         train_data.columns = ['ds', 'y']
19 | 
20 |         model = LinearRegression()
21 |         model.fit(np.arange(1, len(train_data)+1).reshape(-1, 1), train_data.loc[:, 'y'])
22 | 
23 |         y_hat = model.predict(np.arange(1, points_to_predict+1).reshape(-1, 1))
24 | 
25 |         future_dataframe.iloc[-points_to_predict:, 0] = y_hat
26 | 
27 |         return future_dataframe
28 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/lstm.py:
--------------------------------------------------------------------------------
  1 | # import logging
  2 | #
  3 | # from pandas import DataFrame
  4 | # import numpy as np
  5 | # import torch
  6 | # import torch.nn as nn
  7 | # from sklearn.preprocessing import MinMaxScaler
  8 | #
  9 | # from timexseries.data_prediction import PredictionModel
 10 | #
 11 | # log = logging.getLogger(__name__)
 12 | #
 13 | #
 14 | # def split_sequences(df, n_in, n_out, n_features):
 15 | #     in_out_sequence = []
 16 | #
 17 | #     for i in range(len(df)):
 18 | #         # find the end of this pattern
 19 | #         end_ix = i + n_in
 20 | #         out_end_ix = end_ix + n_out
 21 | #         # check if we are beyond the sequence
 22 | #         if out_end_ix > len(df):
 23 | #             break
 24 | #         # gather input and output parts of the pattern
 25 | #         seq_x = []
 26 | #         for j in range(i, end_ix):
 27 | #             this_x = []
 28 | #             for k in range(0, n_features):
 29 | #                 this_x.append(df.iloc[j, k])
 30 | #                 seq_x.append(this_x)
 31 | #
 32 | #         seq_y = list(df.iloc[end_ix:out_end_ix, 0].values)
 33 | #         in_out_sequence.append((seq_x, seq_y))
 34 | #     return in_out_sequence
 35 | #
 36 | #
 37 | # class LSTM(nn.Module):
 38 | #     def __init__(self, input_size=1, hidden_layer_size=10):
 39 | #         super().__init__()
 40 | #         self.hidden_layer_size = hidden_layer_size
 41 | #
 42 | #         self.lstm = nn.LSTM(input_size, hidden_layer_size, num_layers=1)
 43 | #
 44 | #         self.linear = nn.Linear(hidden_layer_size, 1)
 45 | #
 46 | #         self.hidden_cell = (torch.zeros(1, 1, self.hidden_layer_size),
 47 | #                             torch.zeros(1, 1, self.hidden_layer_size))
 48 | #
 49 | #     def forward(self, input_seq):
 50 | #         lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), self.hidden_cell)
 51 | #         predictions = self.linear(lstm_out.view(len(input_seq), -1))
 52 | #         return predictions[-1]
 53 | #
 54 | #
 55 | # class LSTMModel(PredictionModel):
 56 | #     """LSTM prediction model."""
 57 | #     def __init__(self, params: dict, transformation: str = "none"):
 58 | #         super().__init__(params, name="LSTM", transformation=transformation)
 59 | #
 60 | #     def predict(self, train_data: DataFrame, points_to_predict: int,
 61 | #                 future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
 62 | #         """Overrides PredictionModel.predict()"""
 63 | #         if torch.cuda.is_available():
 64 | #             dev = "cuda:0"
 65 | #         else:
 66 | #             dev = "cpu"
 67 | #
 68 | #         scalers = {}
 69 | #
 70 | #         if extra_regressors is not None:
 71 | #             # We could apply self.transformation also on the extra regressors.
 72 | #             # From tests, it looks like it doesn't change much/it worsens the forecasts.
 73 | #             train_data = train_data.join(extra_regressors)
 74 | #             n_features = 1 + len(extra_regressors.columns)
 75 | #             for col in extra_regressors.columns:
 76 | #                 scalers[col] = MinMaxScaler(feature_range=(-1, 1))
 77 | #                 extra_regressors[col] = scalers[col].fit_transform(extra_regressors[[col]])
 78 | #         else:
 79 | #             n_features = 1
 80 | #
 81 | #         train_data.reset_index(inplace=True)
 82 | #         column_indices = [0, 1]
 83 | #         new_names = ['ds', 'y']
 84 | #         old_names = train_data.columns[column_indices]
 85 | #         train_data.rename(columns=dict(zip(old_names, new_names)), inplace=True)
 86 | #         train_data.set_index('ds', inplace=True)
 87 | #
 88 | #         scalers['y'] = MinMaxScaler(feature_range=(-1, 1))
 89 | #         train_data['y'] = scalers['y'].fit_transform(train_data[['y']])
 90 | #
 91 | #         n_steps_in, n_steps_out = round(len(train_data)/4), 1
 92 | #
 93 | #         train_inout_seq = split_sequences(train_data, n_steps_in, n_steps_out, n_features=n_features)
 94 | #
 95 | #         for i in range(0, len(train_inout_seq)):
 96 | #             x = np.array(train_inout_seq[i][0], dtype=np.float32)
 97 | #             y = np.array(train_inout_seq[i][1], dtype=np.float32)
 98 | #             train_inout_seq[i] = (torch.from_numpy(x), torch.tensor(y))
 99 | #
100 | #         model = LSTM(input_size=n_features)
101 | #         model.to(dev)
102 | #
103 | #         loss_function = nn.L1Loss()
104 | #         optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
105 | #
106 | #         epochs = 10
107 | #
108 | #         for i in range(epochs):
109 | #             for seq, labels in train_inout_seq:
110 | #                 seq = seq.to(dev)
111 | #                 labels = labels.to(dev)
112 | #                 optimizer.zero_grad()
113 | #                 model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size).to(dev),
114 | #                                           torch.zeros(1, 1, model.hidden_layer_size).to(dev))
115 | #
116 | #                 y_pred = model(seq)
117 | #
118 | #                 single_loss = loss_function(y_pred, labels)
119 | #                 single_loss.backward()
120 | #                 optimizer.step()
121 | #
122 | #         values_for_prediction = train_inout_seq[-1][0]
123 | #         # self.len_train_set = len(train_data)
124 | #
125 | #         if extra_regressors is not None:
126 | #             extra_regressors = extra_regressors.iloc[-points_to_predict:].copy()
127 | #
128 | #             for col in extra_regressors:
129 | #                 scalers[col] = MinMaxScaler(feature_range=(-1, 1))
130 | #                 extra_regressors[col] = scalers[col].fit_transform(extra_regressors[[col]])
131 | #
132 | #             tensors_to_append = []
133 | #             for i in range(0, points_to_predict):
134 | #                 val = np.array(extra_regressors.iloc[i, :], dtype=np.float32)
135 | #                 tensors_to_append.append(torch.tensor(val))
136 | #
137 | #         x_input = values_for_prediction
138 | #         x_input = x_input.to(dev)
139 | #         model.eval()
140 | #
141 | #         for i in range(points_to_predict):
142 | #             seq = x_input[i:]
143 | #             with torch.no_grad():
144 | #                 model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),
145 | #                                      torch.zeros(1, 1, model.hidden_layer_size))
146 | #                 result = model(seq)
147 | #                 if extra_regressors is not None:
148 | #                     result = torch.cat((result, tensors_to_append[i].to(dev)))
149 | #                 x_input = torch.cat((x_input, result.view(1, -1)))
150 | #
151 | #         results = x_input[-points_to_predict:]
152 | #         results = results.to("cpu")
153 | #         results = [x[0] for x in results]
154 | #         actual_predictions = scalers['y'].inverse_transform(np.array(results).reshape(-1, 1))
155 | #         future_dataframe.iloc[-points_to_predict:, 0] = np.array(actual_predictions).flatten()
156 | #
157 | #         return future_dataframe
158 | #
159 | #
160 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/mockup.py:
--------------------------------------------------------------------------------
 1 | import logging
 2 | 
 3 | from pandas import DataFrame
 4 | 
 5 | from timexseries.data_prediction import PredictionModel
 6 | 
 7 | log = logging.getLogger(__name__)
 8 | 
 9 | 
10 | class MockUpModel(PredictionModel):
11 |     """
12 |     Mock up prediction model. Useful for testing purposes.
13 |     This model "simulates" a real model by returning a - at least in dimensions - correct dataframe.
14 |     This dataframe predicts always 0 is no extra regressors have been given, 1 otherwise.
15 | 
16 |     This can be useful in tests because it runs in very low time and is useful to understand if higher-level functions
17 |     work as intended.
18 |     """
19 | 
20 |     def __init__(self, params: dict, transformation: str = "none"):
21 |         super().__init__(params, name="MockUp", transformation=transformation)
22 |         try:
23 |             if params["model_parameters"]["mockup_confidence"]:
24 |                 self.confidence_intervals = True
25 |             else:
26 |                 self.confidence_intervals = False
27 |         except KeyError:
28 |             self.confidence_intervals = False
29 | 
30 |         try:
31 |             self.forced_predictions = params["model_parameters"]["mockup_forced_predictions"]
32 |         except KeyError:
33 |             self.forced_predictions = None
34 | 
35 |     def predict(self, train_data: DataFrame, points_to_predict: int,
36 |                 future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
37 |         """Overrides PredictionModel.predict()"""
38 |         if self.forced_predictions is not None:
39 |             initial_index = future_dataframe.index[0]
40 |             final_index = future_dataframe.index[-1]
41 |             future_dataframe.loc[:, 'yhat'] = self.forced_predictions.loc[initial_index:final_index]
42 |             if self.confidence_intervals:
43 |                 future_dataframe.loc[:, 'yhat_lower'] = self.forced_predictions.loc[initial_index:final_index].apply(
44 |                     lambda x: x - 0.5
45 |                 )
46 |                 future_dataframe.loc[:, 'yhat_upper'] = self.forced_predictions.loc[initial_index:final_index].apply(
47 |                     lambda x: x + 0.5
48 |                 )
49 |         else:
50 |             if extra_regressors is None:
51 |                 v = 0
52 |             else:
53 |                 v = len(extra_regressors.columns)
54 | 
55 |             future_dataframe.loc[future_dataframe.index[-points_to_predict]:, 'yhat'] = v
56 |             if self.confidence_intervals:
57 |                 future_dataframe.loc[future_dataframe.index[-points_to_predict]:, 'yhat_lower'] = v - 0.5
58 |                 future_dataframe.loc[future_dataframe.index[-points_to_predict]:, 'yhat_upper'] = v + 0.5
59 | 
60 |         return future_dataframe.copy()
61 | 
62 | 
63 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/neuralprophet.py:
--------------------------------------------------------------------------------
  1 | # import logging
  2 | # import os
  3 | # import warnings
  4 | #
  5 | # from neuralprophet import NeuralProphet
  6 | # import pandas as pd
  7 | # from pandas import DataFrame
  8 | #
  9 | # from timexseries.data_prediction import PredictionModel
 10 | # logging.getLogger('nprophet').setLevel(logging.WARNING)
 11 | # log = logging.getLogger(__name__)
 12 | #
 13 | # NeuralProphet.set_log_level("ERROR")
 14 | #
 15 | #
 16 | # class NeuralProphetModel(PredictionModel):
 17 | #     """Facebook's NeuralProphet prediction model."""
 18 | #
 19 | #     def __init__(self, params: dict, transformation: str = "none"):
 20 | #         super().__init__(params, name="NeuralProphet", transformation=transformation)
 21 | #
 22 | #         # Stuff needed to make Prophet shut up during training.
 23 | #         self.suppress_stdout_stderr = suppress_stdout_stderr
 24 | #         try:
 25 | #             self.neuralprophet_parameters = params["model_parameters"]["neuralProphet_parameters"]
 26 | #         except KeyError:
 27 | #             self.neuralprophet_parameters = None
 28 | #
 29 | #     def train(self, input_data: DataFrame, extra_regressors: DataFrame = None):
 30 | #         """Overrides PredictionModel.train()"""
 31 | #
 32 | #         self.neuralprophetmodel = NeuralProphet()
 33 | #         # if self.neuralprophet_parameters is not None:
 34 | #         #     try:
 35 | #         #         timeseries_name = input_data.columns[0]
 36 | #         #         date_format = self.neuralprophet_parameters["holidays_dataframes"]["date_format"]
 37 | #         #         holidays = pd.read_csv(self.neuralprophet_parameters["holidays_dataframes"][timeseries_name])
 38 | #         #         holidays.loc[:, "ds"].apply(lambda x: pd.to_datetime(x, format=date_format))
 39 | #         #         self.neuralprophetmodel = NeuralProphet(holidays=holidays)
 40 | #         #         log.debug(f"Using a dataframe for holidays...")
 41 | #         #     except KeyError:
 42 | #         #         self.neuralprophetmodel = NeuralProphet()
 43 | #         #
 44 | #         #     try:
 45 | #         #         holiday_country = self.neuralprophet_parameters["holiday_country"]
 46 | #         #         self.neuralprophetmodel.add_country_holidays(country_name=holiday_country)
 47 | #         #         log.debug(f"Set {holiday_country} as country for holiday calendar...")
 48 | #         #     except KeyError:
 49 | #         #         pass
 50 | #         #
 51 | #         # else:
 52 | #         # self.neuralprophetmodel = NeuralProphet()
 53 | #
 54 | #         if extra_regressors is not None:
 55 | #             # We could apply self.transformation also on the extra regressors.
 56 | #             # From tests, it looks like it doesn't change much/it worsens the forecasts.
 57 | #             input_data = input_data.join(extra_regressors)
 58 | #             input_data.reset_index(inplace=True)
 59 | #             column_indices = [0, 1]
 60 | #             new_names = ['ds', 'y']
 61 | #             old_names = input_data.columns[column_indices]
 62 | #             input_data.rename(columns=dict(zip(old_names, new_names)), inplace=True)
 63 | #             [self.neuralprophetmodel.add_future_regressor(col) for col in extra_regressors.columns]
 64 | #
 65 | #         else:
 66 | #             input_data.reset_index(inplace=True)
 67 | #             input_data.columns = ['ds', 'y']
 68 | #
 69 | #         with self.suppress_stdout_stderr():
 70 | #             self.neuralprophetmodel.fit(input_data, self.freq)
 71 | #
 72 | #         self.y = input_data
 73 | #
 74 | #     def predict(self, future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
 75 | #         """Overrides PredictionModel.predict()"""
 76 | #         requested_prediction = len(future_dataframe) - len(self.y)
 77 | #
 78 | #         if extra_regressors is not None:
 79 | #             extra_regressors = extra_regressors.iloc[-requested_prediction:, :]
 80 | #             extra_regressors = extra_regressors.reset_index()
 81 | #             extra_regressors = extra_regressors.drop(columns=["index"])
 82 | #
 83 | #         future = self.neuralprophetmodel.make_future_dataframe(self.y, periods=requested_prediction,
 84 | #                                                                n_historic_predictions=len(self.y),
 85 | #                                                                regressors_df=extra_regressors)
 86 | #
 87 | #         with self.suppress_stdout_stderr():
 88 | #             forecast = self.neuralprophetmodel.predict(future)
 89 | #
 90 | #         forecast = forecast.rename(columns={"yhat1": "yhat"})
 91 | #         forecast.set_index('ds', inplace=True)
 92 | #
 93 | #         return forecast
 94 | #
 95 | #
 96 | # class suppress_stdout_stderr(object):
 97 | #     """
 98 | #     A context manager for doing a "deep suppression" of stdout and stderr in
 99 | #     Python, i.e. will suppress all print, even if the print originates in a
100 | #     compiled C/Fortran sub-function.
101 | #        This will not suppress raised exceptions, since exceptions are printed
102 | #     to stderr just before a script exits, and after the context manager has
103 | #     exited (at least, I think that is why it lets exceptions through).
104 | #
105 | #     """
106 | #
107 | #     def __init__(self):
108 | #         # Open a pair of null files
109 | #         self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
110 | #         # Save the actual stdout (1) and stderr (2) file descriptors.
111 | #         self.save_fds = [os.dup(1), os.dup(2)]
112 | #
113 | #     def __enter__(self):
114 | #         # Assign the null pointers to stdout and stderr.
115 | #         os.dup2(self.null_fds[0], 1)
116 | #         os.dup2(self.null_fds[1], 2)
117 | #
118 | #     def __exit__(self, *_):
119 | #         # Re-assign the real stdout/stderr back to (1) and (2)
120 | #         os.dup2(self.save_fds[0], 1)
121 | #         os.dup2(self.save_fds[1], 2)
122 | #         # Close the null files
123 | #         for fd in self.null_fds + self.save_fds:
124 | #             os.close(fd)
125 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/persistence.py:
--------------------------------------------------------------------------------
 1 | import logging
 2 | 
 3 | from pandas import DataFrame
 4 | 
 5 | from timexseries.data_prediction import PredictionModel
 6 | 
 7 | log = logging.getLogger(__name__)
 8 | 
 9 | 
10 | class PersistenceModel(PredictionModel):
11 |     """
12 |     Persistence model predictor (also called *naive* model).
13 |     Its prediction is to use the last known value.
14 |     """
15 | 
16 |     def __init__(self, params: dict, transformation: str = "none"):
17 |         super().__init__(params, name="Persistence", transformation=transformation)
18 | 
19 |     def predict(self, train_data: DataFrame, points_to_predict: int,
20 |                 future_dataframe: DataFrame, extra_regressors: DataFrame = None) -> DataFrame:
21 |         """Overrides PredictionModel.predict()"""
22 |         future_dataframe.iloc[-points_to_predict:, 0] = train_data.iloc[-1, 0]
23 |         return future_dataframe
24 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/prophet.py:
--------------------------------------------------------------------------------
  1 | import logging
  2 | import os
  3 | 
  4 | from prophet import Prophet
  5 | import pandas as pd
  6 | from pandas import DataFrame
  7 | 
  8 | from timexseries.data_prediction import PredictionModel
  9 | 
 10 | logging.getLogger('prophet').setLevel(logging.WARNING)
 11 | stanpy_logger = logging.getLogger('cmdstanpy')
 12 | stanpy_logger.addHandler(logging.NullHandler())
 13 | stanpy_logger.propagate = False
 14 | stanpy_logger.setLevel(logging.CRITICAL)
 15 | 
 16 | log = logging.getLogger(__name__)
 17 | 
 18 | 
 19 | class FBProphetModel(PredictionModel):
 20 |     """Facebook's Prophet prediction model."""
 21 | 
 22 |     def __init__(self, params: dict, transformation: str = "none"):
 23 |         super().__init__(params, name="FBProphet", transformation=transformation)
 24 | 
 25 |         # Stuff needed to make Prophet shut up during training.
 26 |         self.suppress_stdout_stderr = suppress_stdout_stderr
 27 |         fbmodel = Prophet()
 28 |         try:
 29 |             self.fbprophet_parameters = params["model_parameters"]["fbprophet_parameters"]
 30 |         except KeyError:
 31 |             self.fbprophet_parameters = None
 32 | 
 33 |     def predict(self, input_data: DataFrame, points_to_predict: int, future_dataframe: DataFrame, 
 34 |                 extra_regressors: DataFrame = None):
 35 |         """Overrides PredictionModel.predict()"""
 36 | 
 37 |         if self.fbprophet_parameters is not None:
 38 |             try:
 39 |                 timeseries_name = input_data.columns[0]
 40 |                 date_format = self.fbprophet_parameters["holidays_dataframes"]["date_format"]
 41 |                 holidays = pd.read_csv(self.fbprophet_parameters["holidays_dataframes"][timeseries_name])
 42 |                 holidays.loc[:, "ds"].apply(lambda x: pd.to_datetime(x, format=date_format))
 43 |                 fbmodel = Prophet(holidays=holidays)
 44 |                 log.debug(f"Using a dataframe for holidays...")
 45 |             except KeyError:
 46 |                 fbmodel = Prophet()
 47 | 
 48 |             try:
 49 |                 holiday_country = self.fbprophet_parameters["holiday_country"]
 50 |                 fbmodel.add_country_holidays(country_name=holiday_country)
 51 |                 log.debug(f"Set {holiday_country} as country for holiday calendar...")
 52 |             except KeyError:
 53 |                 pass
 54 | 
 55 |         else:
 56 |             fbmodel = Prophet()
 57 | 
 58 |         if extra_regressors is not None:
 59 |             # We could apply self.transformation also on the extra regressors.
 60 |             # From tests, it looks like it doesn't change much/it worsens the forecasts.
 61 |             input_data = input_data.join(extra_regressors)
 62 |             input_data.reset_index(inplace=True)
 63 |             column_indices = [0, 1]
 64 |             new_names = ['ds', 'y']
 65 |             old_names = input_data.columns[column_indices]
 66 |             input_data.rename(columns=dict(zip(old_names, new_names)), inplace=True)
 67 |             [fbmodel.add_regressor(col) for col in extra_regressors.columns]
 68 | 
 69 |         else:
 70 |             input_data.reset_index(inplace=True)
 71 |             input_data.columns = ['ds', 'y']
 72 | 
 73 |         with self.suppress_stdout_stderr():
 74 |             fbmodel.fit(input_data)
 75 | 
 76 |         future = future_dataframe.reset_index()
 77 |         future.rename(columns={'index': 'ds'}, inplace=True)
 78 | 
 79 |         if extra_regressors is not None:
 80 |             future.set_index('ds', inplace=True)
 81 |             future = future.join(extra_regressors.copy())
 82 |             future.reset_index(inplace=True)
 83 | 
 84 |         forecast = fbmodel.predict(future)
 85 | 
 86 |         forecast.set_index('ds', inplace=True)
 87 | 
 88 |         return forecast     
 89 | 
 90 | 
 91 | class suppress_stdout_stderr(object):
 92 |     """
 93 |     A context manager for doing a "deep suppression" of stdout and stderr in
 94 |     Python, i.e. will suppress all print, even if the print originates in a
 95 |     compiled C/Fortran sub-function.
 96 |        This will not suppress raised exceptions, since exceptions are printed
 97 |     to stderr just before a script exits, and after the context manager has
 98 |     exited (at least, I think that is why it lets exceptions through).
 99 | 
100 |     """
101 | 
102 |     def __init__(self):
103 |         # Open a pair of null files
104 |         self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
105 |         # Save the actual stdout (1) and stderr (2) file descriptors.
106 |         self.save_fds = [os.dup(1), os.dup(2)]
107 | 
108 |     def __enter__(self):
109 |         # Assign the null pointers to stdout and stderr.
110 |         os.dup2(self.null_fds[0], 1)
111 |         os.dup2(self.null_fds[1], 2)
112 | 
113 |     def __exit__(self, *_):
114 |         # Re-assign the real stdout/stderr back to (1) and (2)
115 |         os.dup2(self.save_fds[0], 1)
116 |         os.dup2(self.save_fds[1], 2)
117 |         # Close the null files
118 |         for fd in self.null_fds + self.save_fds:
119 |             os.close(fd)
120 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/random_walk_with_drift.py:
--------------------------------------------------------------------------------
 1 | from pandas import DataFrame
 2 | 
 3 | from statsforecast import StatsForecast
 4 | from timexseries.data_prediction import PredictionModel
 5 | from statsforecast.models import AutoARIMA, SeasonalNaive, RandomWalkWithDrift
 6 | 
 7 | from timexseries.data_prediction.models.seasonality_estimator import estimate_seasonality
 8 | 
 9 | 
10 | class RandomWalkWithDriftModel(PredictionModel):
11 |     """Random walk with drift prediction model."""
12 |     def __init__(self, params: dict, transformation: str = "none"):
13 |         super().__init__(params, name="Random walk with drift", transformation=transformation)
14 | 
15 |     def predict(self, train_data: DataFrame, points_to_predict: int,
16 |                 future_dataframe: DataFrame, extra_regressor: DataFrame = None) -> DataFrame:
17 |         freq = train_data.index.freq
18 | 
19 |         train_data.reset_index(inplace=True)
20 |         train_data.columns = ['ds', 'y']
21 |         train_data.loc[:, 'unique_id'] = 0
22 | 
23 |         model = StatsForecast(
24 |             df=train_data,
25 |             models=[RandomWalkWithDrift()],
26 |             freq=freq
27 |         )
28 | 
29 |         y_hat_df = model.forecast(points_to_predict, level=[95]).set_index("ds")
30 |         future_dataframe.iloc[-points_to_predict:, 0] = y_hat_df.loc[:, 'RWD']
31 |         index_to_update = future_dataframe.index[-points_to_predict:]
32 |         future_dataframe.loc[index_to_update, 'yhat_upper'] = y_hat_df.loc[:, 'RWD-hi-95']
33 |         future_dataframe.loc[index_to_update, 'yhat_lower'] = y_hat_df.loc[:, 'RWD-lo-95']
34 | 
35 |         return future_dataframe
36 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/seasonal_persistence.py:
--------------------------------------------------------------------------------
 1 | from pandas import DataFrame
 2 | 
 3 | from statsforecast import StatsForecast
 4 | from timexseries.data_prediction import PredictionModel
 5 | from statsforecast.models import SeasonalNaive
 6 | 
 7 | from timexseries.data_prediction.models.seasonality_estimator import estimate_seasonality
 8 | 
 9 | 
10 | class SeasonalPersistenceModel(PredictionModel):
11 |     """Seasonal persistence (naive) prediction model."""
12 |     def __init__(self, params: dict, transformation: str = "none"):
13 |         super().__init__(params, name="Seasonal persistence", transformation=transformation)
14 | 
15 |     def predict(self, train_data: DataFrame, points_to_predict: int,
16 |                 future_dataframe: DataFrame, extra_regressor: DataFrame = None) -> DataFrame:
17 |         freq = train_data.index.freq
18 | 
19 |         seasonality = estimate_seasonality(train_data)
20 | 
21 |         train_data.reset_index(inplace=True)
22 |         train_data.columns = ['ds', 'y']
23 |         train_data.loc[:, 'unique_id'] = 0
24 | 
25 |         model = StatsForecast(
26 |             df=train_data,
27 |             models=[SeasonalNaive(season_length=seasonality)],
28 |             freq=freq
29 |         )
30 | 
31 |         y_hat_df = model.forecast(points_to_predict, level=[95]).set_index("ds")
32 | 
33 |         future_dataframe.iloc[-points_to_predict:, 0] = y_hat_df.loc[:, 'SeasonalNaive']
34 |         index_to_update = future_dataframe.index[-points_to_predict:]
35 |         future_dataframe.loc[index_to_update, 'yhat_upper'] = y_hat_df.loc[:, 'SeasonalNaive-hi-95']
36 |         future_dataframe.loc[index_to_update, 'yhat_lower'] = y_hat_df.loc[:, 'SeasonalNaive-lo-95']
37 | 
38 |         return future_dataframe
39 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/models/seasonality_estimator.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | import statsmodels.api as sm
 3 | import numpy as np
 4 | 
 5 | 
 6 | def estimate_seasonality(series: pd.DataFrame):
 7 |     """
 8 |     Estimate seasonality in a time-series.
 9 |     Returns seasonality period. Returns 1 if no seasonality is found.
10 |     """
11 | 
12 |     maxnlags = min(30, int(len(series)/2)-1)
13 | 
14 |     s = pd.Series(sm.tsa.pacf(series, method='ywm', nlags=maxnlags))
15 |     s = np.abs(s)
16 | 
17 |     s[0] = 0
18 |     s[1] = 0
19 | 
20 |     s = s[s > 2.58 / np.sqrt(len(series))].sort_values(ascending=False)
21 | 
22 |     if len(s) > 0:
23 |         seasonality = s.index[0] - 1
24 |     else:
25 |         seasonality = 1
26 | 
27 |     return seasonality
28 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/transformation.py:
--------------------------------------------------------------------------------
  1 | from pandas import Series
  2 | import numpy as np
  3 | from scipy.stats import yeojohnson
  4 | 
  5 | 
  6 | class Transformation:
  7 |     """
  8 |     Super-class used to represent various types of data transformation.
  9 |     """
 10 | 
 11 |     def apply(self, data: Series) -> Series:
 12 |         """
 13 |         Apply the transformation on each value in a Pandas Series. Returns the transformed Series, i.e. a Series with
 14 |         transformed values.
 15 | 
 16 |         Note that it is not guaranteed that the dtype of the returned Series is the same of `data`.
 17 | 
 18 |         Parameters
 19 |         ----------
 20 |         data : Series
 21 |             Data to transform.
 22 | 
 23 |         Returns
 24 |         -------
 25 |         Series
 26 |             Transformed data.
 27 |         """
 28 |         pass
 29 | 
 30 |     def inverse(self, data: Series) -> Series:
 31 |         """
 32 |         Apply the inverse of the transformation on the values of a Pandas Series of transformed values.
 33 |         Returns the data re-transformed back to the real world.
 34 | 
 35 |         Any class implementing Transformation should make the `inverse` method always return a Series with the same
 36 |         shape as the one of `data`. If the function is not invertible (e.g. Log), the returning values should be
 37 |         approximated. It is assumed in the rest of TIMEX that `inverse` does not fail.
 38 | 
 39 |         Parameters
 40 |         ----------
 41 |         data : Series
 42 |             Data to transform.
 43 | 
 44 |         Returns
 45 |         -------
 46 |         Series
 47 |             Transformed data.
 48 |         """
 49 |         pass
 50 | 
 51 | 
 52 | class Log(Transformation):
 53 |     """Class corresponding to a somewhat classic logarithmic feature transformation.
 54 | 
 55 |     Notes
 56 |     -----
 57 |     The actual function computed by this transformation is:
 58 | 
 59 |     .. math::
 60 |         f(x) = sign(x) * log(|x|)
 61 | 
 62 |     if `x` > 1, 0 otherwise.
 63 | 
 64 |     Note that this way time-series which contain 0 values will have its values modified, because `inverse` will return
 65 |     1 instead of 0 when returning the transformed time-series to the real world.
 66 | 
 67 |     The inverse function, indeed, is:
 68 | 
 69 |     .. math::
 70 |         f^{-1}(x) = sign(x) * e^{abs(x)}
 71 | 
 72 |     LogModified should be preferred.
 73 |     """
 74 |     def apply(self, data: Series) -> Series:
 75 |         return data.apply(lambda x: np.sign(x) * np.log(abs(x)) if abs(x) > 1 else 0)
 76 | 
 77 |     def inverse(self, data: Series) -> Series:
 78 |         return data.apply(lambda x: np.sign(x) * np.exp(abs(x)))
 79 | 
 80 |     def __str__(self):
 81 |         return "Log"
 82 | 
 83 | 
 84 | class LogModified(Transformation):
 85 |     """Class corresponding to the a custom variant of logarithmic feature transformation.
 86 |     In particular, this transformation tries to overcome the traditional issues of a logarithmic transformation, i.e.
 87 |     the impossibility to work on negative data and the different behaviour on 0 < x < 1.
 88 | 
 89 |     Notes
 90 |     -----
 91 |     The actual function computed by this transformation is:
 92 | 
 93 |     .. math::
 94 |         f(x) = sign(x) * log(|x| + 1)
 95 | 
 96 |     The inverse, instead, is:
 97 | 
 98 |     .. math::
 99 |         f^{-1}(x) = sign(x) * e^{(abs(x) - sign(x))}
100 |     """
101 |     def apply(self, data: Series) -> Series:
102 |         return data.apply(lambda x: np.sign(x) * np.log(abs(x) + 1))
103 | 
104 |     def inverse(self, data: Series) -> Series:
105 |         return data.apply(lambda x: np.sign(x) * np.exp(abs(x)) - np.sign(x))
106 | 
107 |     def __str__(self):
108 |         return "modified Log"
109 | 
110 | 
111 | class Identity(Transformation):
112 |     """Class corresponding to the identity transformation.
113 |     This is useful because the absence of a data pre-processing transformation would be a particular case for functions
114 |     which compute predictions; instead, using this, that case is not special anymore.
115 | 
116 |     Notes
117 |     -----
118 |     The actual function computed by this transformation is:
119 | 
120 |     .. math::
121 |         f(x) = x
122 | 
123 |     The inverse, instead, is:
124 | 
125 |     .. math::
126 |         f^{-1}(x) = x
127 |     """
128 |     def apply(self, data: Series) -> Series:
129 |         return data
130 | 
131 |     def inverse(self, data: Series) -> Series:
132 |         return data
133 | 
134 |     def __str__(self):
135 |         return "none"
136 | 
137 | 
138 | class YeoJohnson(Transformation):
139 |     """Class corresponding to the Yeo-Johnson transformation.
140 | 
141 |     Notes
142 |     -----
143 |     Introduced in [^1], this transformation tries to make the input data more stable.
144 | 
145 |     Warnings
146 |     --------
147 |     .. warning:: Yeo-Johnson is basically broken for some series with high values.
148 |                  Follow this issue: https://github.com/scikit-learn/scikit-learn/issues/14959
149 |                  Until this is solved, Yeo-Johnson may not work as expected and create random crashes.
150 | 
151 |     References
152 |     ----------
153 |     [^1]: Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry.
154 |           Biometrika, 87(4), 954-959. https://doi.org/10.1093/biomet/87.4.954
155 |     """
156 | 
157 |     def __init__(self):
158 |         self.lmbda = 0
159 | 
160 |     def apply(self, data: Series) -> Series:
161 |         ind = data.index
162 |         res, lmbda = yeojohnson(data)
163 |         self.lmbda = lmbda
164 |         return Series(res, index=ind)
165 | 
166 |     def inverse(self, data: Series) -> Series:
167 |         ind = data.index
168 |         lmbda = self.lmbda
169 |         x_inv = np.zeros_like(data)
170 |         pos = data >= 0
171 | 
172 |         # when x >= 0
173 |         if abs(lmbda) < np.spacing(1.):
174 |             x_inv[pos] = np.exp(data[pos]) - 1
175 |         else:  # lmbda != 0
176 |             x_inv[pos] = np.power(data[pos] * lmbda + 1, 1 / lmbda) - 1
177 | 
178 |         # when x < 0
179 |         if abs(lmbda - 2) > np.spacing(1.):
180 |             x_inv[~pos] = 1 - np.power(-(2 - lmbda) * data[~pos] + 1,
181 |                                        1 / (2 - lmbda))
182 |         else:  # lmbda == 2
183 |             x_inv[~pos] = 1 - np.exp(-data[~pos])
184 | 
185 |         return Series(x_inv, index=ind)
186 | 
187 |     def __str__(self):
188 |         return f"Yeo-Johnson (lambda: {round(self.lmbda, 3)})"
189 | 
190 | 
191 | class Diff(Transformation):
192 |     """Class corresponding to the differentiate transformation.
193 |     Basically, each value at time `t` is computed as the difference between the current value and the past one.
194 |     Applying this transformation makes the transformed Series have one less value, because the first one can not be
195 |     computed; the value is saved in order to be able to recompute `inverse`.
196 | 
197 |     Notes
198 |     -----
199 |     Let `X` be the time-series and `X(t)` the value of the time-series at time `t`. This transformation changes X in Y,
200 |     where:
201 | 
202 |     .. math::
203 |         Y(t) = X(t) - X(t-1)
204 |     """
205 |     def __init__(self):
206 |         self.first_value = 0
207 | 
208 |     def apply(self, data: Series) -> Series:
209 |         self.first_value = data[0]
210 |         return data.diff()[1:]
211 | 
212 |     def inverse(self, data: Series) -> Series:
213 |         return Series(np.r_[self.first_value, data].cumsum())
214 | 
215 |     def __str__(self):
216 |         return "differentiate (1)"
217 | 
218 | 
219 | def transformation_factory(tr_class: str) -> Transformation:
220 |     """
221 |     Given the type of the transformation, encoded as string, return the Transformation object.
222 | 
223 |     Parameters
224 |     ----------
225 |     tr_class : str
226 |         Transformation type.
227 | 
228 |     Returns
229 |     -------
230 |     Transformation
231 |         Transformation object.
232 | 
233 |     Examples
234 |     --------
235 |     Create a Pandas Series and apply the logarithmic transformation:
236 | 
237 |     >>> x = Series([2, 3, 4, 5])
238 |     >>> tr = transformation_factory("log")
239 |     >>> tr_x = tr.apply(x)
240 |     >>> tr_x
241 |     0    0.693147
242 |     1    1.098612
243 |     2    1.386294
244 |     3    1.609438
245 |     dtype: float64
246 | 
247 |     Now, let's compute the inverse transformation which should return the data to the real world:
248 | 
249 |     >>> inv_tr_x = tr.inverse(tr_x)
250 |     >>> inv_tr_x
251 |     0    2.0
252 |     1    3.0
253 |     2    4.0
254 |     3    5.0
255 |     dtype: float64
256 |     """
257 |     if tr_class == "log":
258 |         return Log()
259 |     elif tr_class == "log_modified":
260 |         return LogModified()
261 |     elif tr_class == "none":
262 |         return Identity()
263 |     elif tr_class == "diff":
264 |         return Diff()
265 |     elif tr_class == "yeo_johnson":
266 |         return YeoJohnson()
267 | 


--------------------------------------------------------------------------------
/timexseries/data_prediction/xcorr.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | import pandas as pd
  3 | from pandas import DataFrame
  4 | from statsmodels.tsa.stattools import grangercausalitytests
  5 | 
  6 | 
  7 | def calc_xcorr(target: str, ingested_data: DataFrame, max_lags: int, modes: [str] = ["pearson"]) -> dict:
  8 |     """
  9 |     Calculate the cross-correlation for the `ingested data`.
 10 |     Use `target` time-series column as target; the correlation is computed against all lags of all the other columns
 11 |     which include numbers. NaN values, introduced by the various shifts, are replaced with 0.
 12 | 
 13 |     Parameters
 14 |     ----------
 15 |     target : str
 16 |         Column which is used as target for the cross correlation.
 17 | 
 18 |     ingested_data : DataFrame
 19 |         DataFrame which contains the various time-series, one for column.
 20 | 
 21 |     max_lags : int
 22 |         Limit the analysis to max lags.
 23 | 
 24 |     modes : [str]
 25 |         Cross-correlation can be computed with different algorithms. The available choices are:
 26 | 
 27 |         - `matlab_normalized`: same as using the MatLab function xcorr(x, y, 'normalized')
 28 |         - `pearson` : use Pearson formula (NaN values are fillled to 0)
 29 |         - `kendall`: use Kendall formula (NaN values are filled to 0)
 30 |         - `spearman`: use Spearman formula (NaN values are filled to 0)
 31 | 
 32 |     Returns
 33 |     -------
 34 |     result : dict
 35 |         Dictionary with a Pandas DataFrame set for every indicated mode.
 36 |         Each DataFrame has the lags as index and the correlation value for each column.
 37 | 
 38 |     Examples
 39 |     --------
 40 |     Create some sample time-series.
 41 |     >>> dates = pd.date_range('2000-01-01', periods=30)  # Last index is 2000-01-30
 42 |     >>> ds = pd.DatetimeIndex(dates, freq="D")
 43 |     >>>
 44 |     >>> x = np.linspace(0, 2 * np.pi, 60)
 45 |     >>> y = np.sin(x)
 46 |     >>>
 47 |     >>> np.random.seed(0)
 48 |     >>> noise = np.random.normal(0, 2.0, 60)
 49 |     >>> y = y + noise
 50 |     >>>
 51 |     >>> a = y[:30]
 52 |     >>> b = y[5:35]
 53 |     >>>
 54 |     >>> timeseries_dataframe = DataFrame(data={"a": a, "b": b}, index=ds)
 55 | 
 56 |     Compute the cross-correlation:
 57 |     >>> calc_xcorr("a", timeseries_dataframe, 7, ["pearson"])
 58 |     {'pearson':  b
 59 |              -7  0.316213
 60 |              -6 -0.022288
 61 |              -5  0.112483
 62 |              -4 -0.268724
 63 |              -3  0.105511
 64 |              -2  0.178658
 65 |              -1  0.101505
 66 |               0  0.051641
 67 |               1 -0.360475
 68 |               2 -0.074952
 69 |               3 -0.047689
 70 |               4 -0.252324
 71 |               5  0.796120
 72 |               6 -0.170558
 73 |               7 -0.009305
 74 |     }
 75 | 
 76 |     This is expected; the biggest value of cross-correlation is at index `5`. It is true that `b` is exactly time-series
 77 |     `a`, but shifted forward of `5` lags.
 78 |     """
 79 | 
 80 |     def df_shifted(df, _target=None, lag=0):
 81 |         if not lag and not _target:
 82 |             return df
 83 |         new = {}
 84 |         for c in df.columns:
 85 |             if c == _target:
 86 |                 new[c] = df[_target]
 87 |             else:
 88 |                 new[c] = df[c].shift(periods=lag)
 89 |         return pd.DataFrame(data=new)
 90 | 
 91 |     columns = ingested_data.columns.tolist()
 92 |     columns = [elem for elem in columns if ingested_data[elem].dtype != str and elem != target]
 93 | 
 94 |     results = {}
 95 |     for mode in modes:
 96 |         result = DataFrame(columns=columns, dtype=np.float64)
 97 |         if mode == 'matlab_normalized':
 98 |             lags_to_displace = max_lags if max_lags < len(ingested_data) else len(ingested_data) - 1
 99 |             for col in columns:
100 |                 x = ingested_data[target]
101 |                 y = ingested_data[col]
102 | 
103 |                 c = np.correlate(x, y, mode="full")
104 | 
105 |                 # This is needed to obtain the same result of the MatLab `xcorr` function with normalized results.
106 |                 # You can find the formula in the function pyplot.xcorr; however, here the property
107 |                 # sqrt(x*y) = sqrt(x) * sqrt(y)
108 |                 # is applied in order to avoid overflows if the ingested values are particularly high.
109 |                 den = np.sqrt(np.dot(x, x)) * np.sqrt(np.dot(y, y))
110 |                 c = np.divide(c, den)
111 | 
112 |                 # This assigns the correct indexes to the results.
113 |                 if len(ingested_data) > max_lags:
114 |                     c = c[len(ingested_data) - 1 - max_lags:len(ingested_data) + max_lags]
115 | 
116 |                 result[col] = c
117 | 
118 |             result.index -= lags_to_displace
119 | 
120 |         elif mode == 'granger':
121 |             for col in columns:
122 |                 granger_max_lags = int(len(ingested_data) / 3) - 1
123 |                 granger_max_lags = granger_max_lags if granger_max_lags < max_lags else max_lags
124 | 
125 |                 # Trick to compute both negative and positive lags
126 |                 df = ingested_data[[col, target]]
127 |                 granger_result = grangercausalitytests(df, maxlag=granger_max_lags, verbose=False)
128 |                 for i in granger_result:
129 |                     result.loc[-i, col] = 1 - granger_result[i][0]['params_ftest'][1]
130 | 
131 |                 df = ingested_data[[target, col]]
132 |                 granger_result = grangercausalitytests(df, maxlag=granger_max_lags, verbose=False)
133 |                 for i in granger_result:
134 |                     result.loc[i, col] = 1 - granger_result[i][0]['params_ftest'][1]
135 | 
136 |             result.sort_index(inplace=True)
137 | 
138 |         else:
139 |             lags_to_displace = max_lags if max_lags < len(ingested_data) else len(ingested_data)
140 |             for i in range(-lags_to_displace, lags_to_displace + 1):
141 |                 shifted = df_shifted(ingested_data, target, i)
142 |                 shifted.fillna(0, inplace=True)
143 | 
144 |                 corr = [shifted[target].corr(other=shifted[col], method=mode) for col in columns]
145 |                 result.loc[i] = corr
146 | 
147 |         results[mode] = result
148 | 
149 |     return results
150 | 
151 | 
152 | def calc_all_xcorr(ingested_data: DataFrame, param_config: dict) -> dict:
153 |     """
154 |     Compute, for every column in `ingested_data` (excluding the index) the cross-correlation of that series with respect
155 |     to all others columns in ingested data.
156 | 
157 |     Parameters
158 |     ----------
159 |     ingested_data : DataFrame
160 |         Pandas DataFrame for which the cross-correlation of all columns should be computed.
161 | 
162 |     param_config : dict
163 |         TIMEX configuration dictionary, needed to for `xcorr_parameters`.
164 |         In the `xcorr_parameters` sub-dictionary, `xcorr_modes` and `xcorr_max_lags` will be used.
165 |         `xcorr_modes` indicate the different algorithms which should be used to compute the cross-correlation.
166 |         The available choices are:
167 | 
168 |         - `matlab_normalized`: same as using the MatLab function xcorr(x, y, 'normalized')
169 |         - `pearson` : use Pearson formula (NaN values are fillled to 0)
170 |         - `kendall`: use Kendall formula (NaN values are filled to 0)
171 |         - `spearman`: use Spearman formula (NaN values are filled to 0)
172 | 
173 |         `xcorr_max_lags` is the number of lags, both in positive and negative direction, to which the cross-correlation
174 |         calculations should be limited to.
175 | 
176 |     Returns
177 |     -------
178 |     dict
179 |         Python dict with a key for every time-series in `ingested_data`; every key will correspond to another dictionary
180 |         with one entry for each cross-correlation algorithm requested.
181 | 
182 |     Examples
183 |     --------
184 |     Create sample data.
185 |     >>> dates = pd.date_range('2000-01-01', periods=30)  # Last index is 2000-01-30
186 |     >>> ds = pd.DatetimeIndex(dates, freq="D")
187 |     >>>
188 |     >>> x = np.linspace(0, 2 * np.pi, 60)
189 |     >>> y = np.sin(x)
190 |     >>> np.random.seed(0)
191 |     >>> noise = np.random.normal(0, 2.0, 60)
192 |     >>> y = y + noise
193 |     >>>
194 |     >>> a = y[:30]
195 |     >>> b = y[2:32]
196 |     >>> c = y[4:34]
197 |     >>>
198 |     >>> timeseries_dataframe = DataFrame(data={"a": a, "b": b, "c": c}, index=ds)
199 | 
200 |     Compute the cross-correlation on this DataFrame:
201 |     >>> param_config = {
202 |     >>>     "xcorr_parameters": {
203 |     >>>         "xcorr_max_lags": 2,
204 |     >>>         "xcorr_mode": "pearson,matlab_normalized"
205 |     >>>     }
206 |     >>> }
207 |     >>> calc_all_xcorr(timeseries_dataframe, param_config)
208 |     {'a': {'pearson':            b         c
209 |                              -2 -0.252086  0.117286
210 |                              -1  0.006370  0.064624
211 |                               0 -0.011866 -0.290049
212 |                               1 -0.115114 -0.091762
213 |                               2  0.951782 -0.024158,
214 |            'matlab_normalized':  b         c
215 |                              -2  0.109634  0.287681
216 |                              -1  0.314318  0.239430
217 |                              0   0.319016  0.008663
218 |                              1   0.244525  0.197663
219 |                              2   0.965095  0.260254},
220 | 
221 |      'b': {'pearson':            a         c
222 |                              -2  0.998491 -0.353341
223 |                              -1 -0.085531 -0.007476
224 |                               0 -0.011866  0.048841
225 |                               1  0.013242 -0.092448
226 |                               2 -0.258411  0.895226,
227 |            'matlab_normalized':  a         c
228 |                              -2  0.965095 -0.063331
229 |                              -1  0.244525  0.177921
230 |                              0   0.319016  0.252201
231 |                              1   0.314318  0.183260
232 |                              2   0.109634  0.862899},
233 | 
234 |      'c': {'pearson':            a         b
235 |                              -2  0.076014  0.929572
236 |                              -1 -0.013978 -0.026488
237 |                               0 -0.290049  0.048841
238 |                               1  0.038452 -0.043913
239 |                               2  0.125275 -0.354749,
240 |            'matlab_normalized':  a         b
241 |                              -2  0.260254  0.862899
242 |                              -1  0.197663  0.183260
243 |                              0   0.008663  0.252201
244 |                              1   0.239430  0.177921
245 |                              2   0.287681 -0.063331}}
246 |     """
247 |     xcorr_max_lags = param_config['xcorr_parameters']['xcorr_max_lags']
248 |     xcorr_modes = [*param_config['xcorr_parameters']["xcorr_mode"].split(",")]
249 |     d = {}
250 |     for col in ingested_data.columns:
251 |         d[col] = calc_xcorr(col, ingested_data, max_lags=xcorr_max_lags, modes=xcorr_modes)
252 | 
253 |     return d


--------------------------------------------------------------------------------
/timexseries/data_visualization/__init__.py:
--------------------------------------------------------------------------------
1 | from .functions import create_timeseries_dash_children


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/timexseries/data_visualization/locales/base.pot:
--------------------------------------------------------------------------------
  1 | # Translations template for PROJECT.
  2 | # Copyright (C) 2022 ORGANIZATION
  3 | # This file is distributed under the same license as the PROJECT project.
  4 | # FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
  5 | #
  6 | #, fuzzy
  7 | msgid ""
  8 | msgstr ""
  9 | "Project-Id-Version: PROJECT VERSION\n"
 10 | "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
 11 | "POT-Creation-Date: 2022-07-26 18:41+0200\n"
 12 | "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 13 | "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 14 | "Language-Team: LANGUAGE <LL@li.org>\n"
 15 | "MIME-Version: 1.0\n"
 16 | "Content-Type: text/plain; charset=utf-8\n"
 17 | "Content-Transfer-Encoding: 8bit\n"
 18 | "Generated-By: Babel 2.10.3\n"
 19 | 
 20 | #: functions.py:99
 21 | msgid " analysis"
 22 | msgstr ""
 23 | 
 24 | #: functions.py:100
 25 | msgid "Data visualization"
 26 | msgstr ""
 27 | 
 28 | #: functions.py:115
 29 | msgid "Cross-correlation"
 30 | msgstr ""
 31 | 
 32 | #: functions.py:116
 33 | msgid ""
 34 | "Negative lags (left part) show the correlation between this scenario and "
 35 | "the future of the others."
 36 | msgstr ""
 37 | 
 38 | #: functions.py:118
 39 | msgid ""
 40 | "Meanwhile, positive lags (right part) shows the correlation between this "
 41 | "scenario and the past of the others."
 42 | msgstr ""
 43 | 
 44 | #: functions.py:121
 45 | msgid ""
 46 | "The peaks found using each cross-correlation modality are shown in the "
 47 | "graphs:"
 48 | msgstr ""
 49 | 
 50 | #: functions.py:132
 51 | msgid "Training & Validation results"
 52 | msgstr ""
 53 | 
 54 | #: functions.py:162
 55 | msgid "Prediction"
 56 | msgstr ""
 57 | 
 58 | #: functions.py:163
 59 | msgid "For every model the best predictions for each past date are plotted."
 60 | msgstr ""
 61 | 
 62 | #: functions.py:221 functions.py:249
 63 | msgid "Line plot"
 64 | msgstr ""
 65 | 
 66 | #: functions.py:287
 67 | msgid "Histogram"
 68 | msgstr ""
 69 | 
 70 | #: functions.py:287 functions.py:689 functions.py:783
 71 | msgid "Count"
 72 | msgstr ""
 73 | 
 74 | #: functions.py:319
 75 | msgid "Trend"
 76 | msgstr ""
 77 | 
 78 | #: functions.py:319
 79 | msgid "Seasonality"
 80 | msgstr ""
 81 | 
 82 | #: functions.py:319
 83 | msgid "Residual"
 84 | msgstr ""
 85 | 
 86 | #: functions.py:353
 87 | msgid "Components decomposition"
 88 | msgstr ""
 89 | 
 90 | #: functions.py:353
 91 | msgid "Decomposition model"
 92 | msgstr ""
 93 | 
 94 | #: functions.py:354
 95 | msgid "Additive"
 96 | msgstr ""
 97 | 
 98 | #: functions.py:355
 99 | msgid "Multiplicative"
100 | msgstr ""
101 | 
102 | #: functions.py:361
103 | msgid ""
104 | "Multiplicative model is not available for series which contain zero or "
105 | "negative values."
106 | msgstr ""
107 | 
108 | #: functions.py:406
109 | msgid "autocorrelation"
110 | msgstr ""
111 | 
112 | #: functions.py:411
113 | msgid "Autocorrelation plot"
114 | msgstr ""
115 | 
116 | #: functions.py:411 functions.py:472
117 | msgid "Lags"
118 | msgstr ""
119 | 
120 | #: functions.py:411
121 | msgid "Autocorrelation"
122 | msgstr ""
123 | 
124 | #: functions.py:471
125 | msgid "Cross-correlation using different algorithms"
126 | msgstr ""
127 | 
128 | #: functions.py:473
129 | msgid "Correlation"
130 | msgstr ""
131 | 
132 | #: functions.py:584
133 | msgid "Lag: "
134 | msgstr ""
135 | 
136 | #: functions.py:689
137 | msgid "Box plot"
138 | msgstr ""
139 | 
140 | #: functions.py:691
141 | msgid "The index refer to the end of the period."
142 | msgstr ""
143 | 
144 | #: functions.py:728
145 | msgid "minute"
146 | msgstr ""
147 | 
148 | #: functions.py:728
149 | msgid "hour"
150 | msgstr ""
151 | 
152 | #: functions.py:728
153 | msgid "weekday"
154 | msgstr ""
155 | 
156 | #: functions.py:728
157 | msgid "day"
158 | msgstr ""
159 | 
160 | #: functions.py:728
161 | msgid "month"
162 | msgstr ""
163 | 
164 | #: functions.py:728
165 | msgid "year"
166 | msgstr ""
167 | 
168 | #: functions.py:783
169 | msgid "Aggregate box plot"
170 | msgstr ""
171 | 
172 | #: functions.py:835
173 | msgid "unused data"
174 | msgstr ""
175 | 
176 | #: functions.py:839 functions.py:1029
177 | msgid "training data"
178 | msgstr ""
179 | 
180 | #: functions.py:846
181 | msgid "validation data"
182 | msgstr ""
183 | 
184 | #: functions.py:851
185 | msgid "yhat"
186 | msgstr ""
187 | 
188 | #: functions.py:855
189 | msgid "yhatlower"
190 | msgstr ""
191 | 
192 | #: functions.py:858
193 | msgid "yhatupper"
194 | msgstr ""
195 | 
196 | #: functions.py:862
197 | msgid "Best prediction for the validation set"
198 | msgstr ""
199 | 
200 | #: functions.py:907
201 | msgid "This model, during the history, reached these performances on unseen data:"
202 | msgstr ""
203 | 
204 | #: functions.py:909
205 | msgid "Range of validation data: "
206 | msgstr ""
207 | 
208 | #: functions.py:915
209 | msgid "real data"
210 | msgstr ""
211 | 
212 | #: functions.py:920
213 | msgid "historical prediction"
214 | msgstr ""
215 | 
216 | #: functions.py:928
217 | msgid "future yhat"
218 | msgstr ""
219 | 
220 | #: functions.py:934 functions.py:937
221 | msgid "Error series"
222 | msgstr ""
223 | 
224 | #: functions.py:936 functions.py:940
225 | msgid "Historical prediction"
226 | msgstr ""
227 | 
228 | #: functions.py:949 functions.py:957
229 | msgid "Error series histogram"
230 | msgstr ""
231 | 
232 | #: functions.py:1042
233 | msgid "Performances with different training windows"
234 | msgstr ""
235 | 
236 | #: functions.py:1090
237 | msgid "Model type: "
238 | msgstr ""
239 | 
240 | #: functions.py:1091
241 | msgid "Values used for validation: last "
242 | msgstr ""
243 | 
244 | #: functions.py:1091
245 | msgid " values"
246 | msgstr ""
247 | 
248 | #: functions.py:1092
249 | msgid "The length of the training windows is the "
250 | msgstr ""
251 | 
252 | #: functions.py:1093
253 | msgid " of the length of the time series."
254 | msgstr ""
255 | 
256 | #: functions.py:1094
257 | msgid "Training windows are composed of "
258 | msgstr ""
259 | 
260 | #: functions.py:1094
261 | msgid " values."
262 | msgstr ""
263 | 
264 | #: functions.py:1095
265 | msgid "The model has used "
266 | msgstr ""
267 | 
268 | #: functions.py:1095
269 | msgid " as extra-regressor(s) to improve the training."
270 | msgstr ""
271 | 
272 | #: functions.py:1096
273 | msgid "The model has used a "
274 | msgstr ""
275 | 
276 | #: functions.py:1096
277 | msgid " transformation on the input data."
278 | msgstr ""
279 | 
280 | #: functions.py:1098
281 | msgid "The model has not used any pre/post transformation on input data."
282 | msgstr ""
283 | 
284 | #: functions.py:1102
285 | msgid "Model characteristics:"
286 | msgstr ""
287 | 
288 | #: functions.py:1104
289 | msgid "This model, using the best training window, reaches these performances:"
290 | msgstr ""
291 | 
292 | #: functions.py:1139
293 | msgid "Arithmetic mean of errors:"
294 | msgstr ""
295 | 
296 | #: functions.py:1140
297 | msgid "Standard deviation of errors: "
298 | msgstr ""
299 | 
300 | 


--------------------------------------------------------------------------------
/timexseries/data_visualization/locales/en/LC_MESSAGES/messages.mo:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/DhiriaDev/TIMEX/767841a830bdc6fafbe1702700b9f80175e357de/timexseries/data_visualization/locales/en/LC_MESSAGES/messages.mo


--------------------------------------------------------------------------------
/timexseries/data_visualization/locales/en/LC_MESSAGES/messages.po:
--------------------------------------------------------------------------------
  1 | 
  2 | msgid ""
  3 | msgstr ""
  4 | "Project-Id-Version: PACKAGE VERSION\n"
  5 | "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
  6 | "POT-Creation-Date: 2022-07-26 18:41+0200\n"
  7 | "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
  8 | "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
  9 | "Language: en\n"
 10 | "Language-Team: English <LL@li.org>\n"
 11 | "Plural-Forms: nplurals=2; plural=(n != 1);\n"
 12 | "MIME-Version: 1.0\n"
 13 | "Content-Type: text/plain; charset=utf-8\n"
 14 | "Content-Transfer-Encoding: 8bit\n"
 15 | "Generated-By: Babel 2.10.3\n"
 16 | 
 17 | #: functions.py:99
 18 | msgid " analysis"
 19 | msgstr " analysis"
 20 | 
 21 | #: functions.py:100
 22 | msgid "Data visualization"
 23 | msgstr "Data visualization"
 24 | 
 25 | #: functions.py:115
 26 | msgid "Cross-correlation"
 27 | msgstr "Cross-correlation"
 28 | 
 29 | #: functions.py:116
 30 | msgid ""
 31 | "Negative lags (left part) show the correlation between this scenario and "
 32 | "the future of the others."
 33 | msgstr ""
 34 | "Negative lags (left part) show the correlation between this scenario and "
 35 | "the future of the others."
 36 | 
 37 | #: functions.py:118
 38 | msgid ""
 39 | "Meanwhile, positive lags (right part) shows the correlation between this "
 40 | "scenario and the past of the others."
 41 | msgstr ""
 42 | "Meanwhile, positive lags (right part) shows the correlation between this "
 43 | "scenario and the past of the others."
 44 | 
 45 | #: functions.py:121
 46 | msgid ""
 47 | "The peaks found using each cross-correlation modality are shown in the "
 48 | "graphs:"
 49 | msgstr ""
 50 | "The peaks found using each cross-correlation modality are shown in the "
 51 | "graphs:"
 52 | 
 53 | #: functions.py:132
 54 | msgid "Training & Validation results"
 55 | msgstr "Training & Validation results"
 56 | 
 57 | #: functions.py:162
 58 | msgid "Prediction"
 59 | msgstr "Prediction"
 60 | 
 61 | #: functions.py:163
 62 | msgid "For every model the best predictions for each past date are plotted."
 63 | msgstr "For every model the best predictions for each past date are plotted."
 64 | 
 65 | #: functions.py:221 functions.py:249
 66 | msgid "Line plot"
 67 | msgstr "Line plot"
 68 | 
 69 | #: functions.py:287
 70 | msgid "Histogram"
 71 | msgstr "Histogram"
 72 | 
 73 | #: functions.py:287 functions.py:689 functions.py:783
 74 | msgid "Count"
 75 | msgstr "Count"
 76 | 
 77 | #: functions.py:319
 78 | msgid "Trend"
 79 | msgstr "Trend"
 80 | 
 81 | #: functions.py:319
 82 | msgid "Seasonality"
 83 | msgstr "Seasonality"
 84 | 
 85 | #: functions.py:319
 86 | msgid "Residual"
 87 | msgstr "Residual"
 88 | 
 89 | #: functions.py:353
 90 | msgid "Components decomposition"
 91 | msgstr "Components decomposition"
 92 | 
 93 | #: functions.py:353
 94 | msgid "Decomposition model"
 95 | msgstr "Decomposition model"
 96 | 
 97 | #: functions.py:354
 98 | msgid "Additive"
 99 | msgstr "Additive"
100 | 
101 | #: functions.py:355
102 | msgid "Multiplicative"
103 | msgstr "Multiplicative"
104 | 
105 | #: functions.py:361
106 | msgid ""
107 | "Multiplicative model is not available for series which contain zero or "
108 | "negative values."
109 | msgstr ""
110 | "Multiplicative model is not available for series which contain zero or "
111 | "negative values."
112 | 
113 | #: functions.py:406
114 | msgid "autocorrelation"
115 | msgstr "Autocorrelation"
116 | 
117 | #: functions.py:411
118 | msgid "Autocorrelation plot"
119 | msgstr "Autocorrelation plot"
120 | 
121 | #: functions.py:411 functions.py:472
122 | msgid "Lags"
123 | msgstr "Lags"
124 | 
125 | #: functions.py:411
126 | msgid "Autocorrelation"
127 | msgstr "Autocorrelation"
128 | 
129 | #: functions.py:471
130 | msgid "Cross-correlation using different algorithms"
131 | msgstr "Cross-correlation using different algorithms"
132 | 
133 | #: functions.py:473
134 | msgid "Correlation"
135 | msgstr "Correlation"
136 | 
137 | #: functions.py:584
138 | msgid "Lag: "
139 | msgstr "Lag: "
140 | 
141 | #: functions.py:689
142 | msgid "Box plot"
143 | msgstr "Box plot"
144 | 
145 | #: functions.py:691
146 | msgid "The index refer to the end of the period."
147 | msgstr "The index refers to the end of the period."
148 | 
149 | #: functions.py:728
150 | msgid "minute"
151 | msgstr "Minute"
152 | 
153 | #: functions.py:728
154 | msgid "hour"
155 | msgstr "Hour"
156 | 
157 | #: functions.py:728
158 | msgid "weekday"
159 | msgstr "Weekday"
160 | 
161 | #: functions.py:728
162 | msgid "day"
163 | msgstr "Day of month"
164 | 
165 | #: functions.py:728
166 | msgid "month"
167 | msgstr "Month"
168 | 
169 | #: functions.py:728
170 | msgid "year"
171 | msgstr "Year"
172 | 
173 | #: functions.py:783
174 | msgid "Aggregate box plot"
175 | msgstr "Aggregate box plot"
176 | 
177 | #: functions.py:835
178 | msgid "unused data"
179 | msgstr "Unused data"
180 | 
181 | #: functions.py:839 functions.py:1029
182 | msgid "training data"
183 | msgstr "Training data"
184 | 
185 | #: functions.py:846
186 | msgid "validation data"
187 | msgstr "Validation data"
188 | 
189 | #: functions.py:851
190 | msgid "yhat"
191 | msgstr "Prediction"
192 | 
193 | #: functions.py:855
194 | msgid "yhatlower"
195 | msgstr "Prediction (lowerbound)"
196 | 
197 | #: functions.py:858
198 | msgid "yhatupper"
199 | msgstr "Prediction (upperbound)"
200 | 
201 | #: functions.py:862
202 | msgid "Best prediction for the validation set"
203 | msgstr "Best prediction for the validation set"
204 | 
205 | #: functions.py:907
206 | msgid "This model, during the history, reached these performances on unseen data:"
207 | msgstr "This model, during the history, reached these performances on unseen data:"
208 | 
209 | #: functions.py:909
210 | msgid "Range of validation data: "
211 | msgstr "Range of validation data: "
212 | 
213 | #: functions.py:915
214 | msgid "real data"
215 | msgstr "Real data"
216 | 
217 | #: functions.py:920
218 | msgid "historical prediction"
219 | msgstr "Historical prediction"
220 | 
221 | #: functions.py:928
222 | msgid "future yhat"
223 | msgstr "Future prediction"
224 | 
225 | #: functions.py:934 functions.py:937
226 | msgid "Error series"
227 | msgstr "Error series"
228 | 
229 | #: functions.py:936 functions.py:940
230 | msgid "Historical prediction"
231 | msgstr "Historical prediction"
232 | 
233 | #: functions.py:949 functions.py:957
234 | msgid "Error series histogram"
235 | msgstr "Error series histogram"
236 | 
237 | #: functions.py:1042
238 | msgid "Performances with different training windows"
239 | msgstr "Performances with different training windows"
240 | 
241 | #: functions.py:1090
242 | msgid "Model type: "
243 | msgstr "Model type: "
244 | 
245 | #: functions.py:1091
246 | #, fuzzy
247 | msgid "Values used for validation: last "
248 | msgstr "Values used for validation: last "
249 | 
250 | #: functions.py:1091
251 | msgid " values"
252 | msgstr " values"
253 | 
254 | #: functions.py:1092
255 | msgid "The length of the training windows is the "
256 | msgstr "The length of the training windows is the "
257 | 
258 | #: functions.py:1093
259 | msgid " of the length of the time series."
260 | msgstr " of the length of the time series."
261 | 
262 | #: functions.py:1094
263 | msgid "Training windows are composed of "
264 | msgstr "Training windows are composed of "
265 | 
266 | #: functions.py:1094
267 | msgid " values."
268 | msgstr " values."
269 | 
270 | #: functions.py:1095
271 | msgid "The model has used "
272 | msgstr "The model has used "
273 | 
274 | #: functions.py:1095
275 | msgid " as extra-regressor(s) to improve the training."
276 | msgstr " as extra-regressor(s) to improve the training."
277 | 
278 | #: functions.py:1096
279 | msgid "The model has used a "
280 | msgstr "The model has used a "
281 | 
282 | #: functions.py:1096
283 | msgid " transformation on the input data."
284 | msgstr " transformation on the input data."
285 | 
286 | #: functions.py:1098
287 | msgid "The model has not used any pre/post transformation on input data."
288 | msgstr "The model has not used any pre/post transformation on input data."
289 | 
290 | #: functions.py:1102
291 | msgid "Model characteristics:"
292 | msgstr "Model characteristics:"
293 | 
294 | #: functions.py:1104
295 | msgid "This model, using the best training window, reaches these performances:"
296 | msgstr "This model, using the best training window, reaches these performances:"
297 | 
298 | #: functions.py:1139
299 | msgid "Arithmetic mean of errors:"
300 | msgstr "Arithmetic mean of errors: "
301 | 
302 | #: functions.py:1140
303 | msgid "Standard deviation of errors: "
304 | msgstr "Standard deviation of errors: "
305 | 
306 | 


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/timexseries/data_visualization/locales/it/LC_MESSAGES/messages.mo:
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https://raw.githubusercontent.com/DhiriaDev/TIMEX/767841a830bdc6fafbe1702700b9f80175e357de/timexseries/data_visualization/locales/it/LC_MESSAGES/messages.mo


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/timexseries/data_visualization/locales/it/LC_MESSAGES/messages.po:
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  1 | # SOME DESCRIPTIVE TITLE.
  2 | # Copyright (C) 2021 ORGANIZATION
  3 | # FIRST AUTHOR <EMAIL@ADDRESS>, 2021.
  4 | #
  5 | msgid ""
  6 | msgstr ""
  7 | "Project-Id-Version: PACKAGE VERSION\n"
  8 | "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
  9 | "POT-Creation-Date: 2022-07-26 18:41+0200\n"
 10 | "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 11 | "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 12 | "Language: it\n"
 13 | "Language-Team: Italian <LL@li.org>\n"
 14 | "Plural-Forms: nplurals=2; plural=(n != 1);\n"
 15 | "MIME-Version: 1.0\n"
 16 | "Content-Type: text/plain; charset=utf-8\n"
 17 | "Content-Transfer-Encoding: 8bit\n"
 18 | "Generated-By: Babel 2.10.3\n"
 19 | 
 20 | #: functions.py:99
 21 | msgid " analysis"
 22 | msgstr " analisi"
 23 | 
 24 | #: functions.py:100
 25 | msgid "Data visualization"
 26 | msgstr "Visualizzazione dei dati"
 27 | 
 28 | #: functions.py:115
 29 | msgid "Cross-correlation"
 30 | msgstr "Cross-correlazione"
 31 | 
 32 | #: functions.py:116
 33 | msgid ""
 34 | "Negative lags (left part) show the correlation between this scenario and "
 35 | "the future of the others."
 36 | msgstr ""
 37 | "I ritardi negativi (parte sinistra) mostrano la correlazione tra questo "
 38 | "scenario e il futuro degli altri."
 39 | 
 40 | #: functions.py:118
 41 | msgid ""
 42 | "Meanwhile, positive lags (right part) shows the correlation between this "
 43 | "scenario and the past of the others."
 44 | msgstr ""
 45 | "Invece, i ritardi positivi (parte destra) mostrano la correlazione tra "
 46 | "questo scenario e il passato degli altri."
 47 | 
 48 | #: functions.py:121
 49 | msgid ""
 50 | "The peaks found using each cross-correlation modality are shown in the "
 51 | "graphs:"
 52 | msgstr ""
 53 | "I picchi raggiunti usando ciascuna modalità di cross-correlazione sono "
 54 | "mostrati nel/nei grafo/grafi:"
 55 | 
 56 | #: functions.py:132
 57 | msgid "Training & Validation results"
 58 | msgstr "Risultati dell'allenamento del modello"
 59 | 
 60 | #: functions.py:162
 61 | msgid "Prediction"
 62 | msgstr "Predizione"
 63 | 
 64 | #: functions.py:163
 65 | msgid "For every model the best predictions for each past date are plotted."
 66 | msgstr ""
 67 | "Per ciascun modello vengono mostrate le migliori predizioni ottenibili in"
 68 | " ciascuna data passata."
 69 | 
 70 | #: functions.py:221 functions.py:249
 71 | msgid "Line plot"
 72 | msgstr "Grafico"
 73 | 
 74 | #: functions.py:287
 75 | msgid "Histogram"
 76 | msgstr "Istogramma"
 77 | 
 78 | #: functions.py:287 functions.py:689 functions.py:783
 79 | msgid "Count"
 80 | msgstr "Conteggio"
 81 | 
 82 | #: functions.py:319
 83 | msgid "Trend"
 84 | msgstr "Trend"
 85 | 
 86 | #: functions.py:319
 87 | msgid "Seasonality"
 88 | msgstr "Stagionalità"
 89 | 
 90 | #: functions.py:319
 91 | msgid "Residual"
 92 | msgstr "Residuo"
 93 | 
 94 | #: functions.py:353
 95 | msgid "Components decomposition"
 96 | msgstr "Decomposizione in componenti"
 97 | 
 98 | #: functions.py:353
 99 | msgid "Decomposition model"
100 | msgstr "Modello di decomposizione"
101 | 
102 | #: functions.py:354
103 | msgid "Additive"
104 | msgstr "Additivo"
105 | 
106 | #: functions.py:355
107 | msgid "Multiplicative"
108 | msgstr "Moltiplicativo"
109 | 
110 | #: functions.py:361
111 | msgid ""
112 | "Multiplicative model is not available for series which contain zero or "
113 | "negative values."
114 | msgstr ""
115 | "Il modello moltiplicativo non è disponibile in serie che contengono "
116 | "valori nulli o negativi."
117 | 
118 | #: functions.py:406
119 | msgid "autocorrelation"
120 | msgstr "Autocorrelazione"
121 | 
122 | #: functions.py:411
123 | msgid "Autocorrelation plot"
124 | msgstr "Grafico dell'autocorrelazione"
125 | 
126 | #: functions.py:411 functions.py:472
127 | msgid "Lags"
128 | msgstr "Ritardi"
129 | 
130 | #: functions.py:411
131 | msgid "Autocorrelation"
132 | msgstr "Autocorrelazione"
133 | 
134 | #: functions.py:471
135 | msgid "Cross-correlation using different algorithms"
136 | msgstr "Cross-correlazione usando vari algoritmi"
137 | 
138 | #: functions.py:473
139 | msgid "Correlation"
140 | msgstr "Correlazione"
141 | 
142 | #: functions.py:584
143 | msgid "Lag: "
144 | msgstr "Ritardi: "
145 | 
146 | #: functions.py:689
147 | msgid "Box plot"
148 | msgstr "Diagramma a scatola"
149 | 
150 | #: functions.py:691
151 | msgid "The index refer to the end of the period."
152 | msgstr "L'indice fa riferimento alla fine del periodo considerato."
153 | 
154 | #: functions.py:728
155 | msgid "minute"
156 | msgstr "Minuto"
157 | 
158 | #: functions.py:728
159 | msgid "hour"
160 | msgstr "Ora"
161 | 
162 | #: functions.py:728
163 | msgid "weekday"
164 | msgstr "Giorno della settimana"
165 | 
166 | #: functions.py:728
167 | msgid "day"
168 | msgstr "Giorno del mese"
169 | 
170 | #: functions.py:728
171 | msgid "month"
172 | msgstr "Mese"
173 | 
174 | #: functions.py:728
175 | msgid "year"
176 | msgstr "Anno"
177 | 
178 | #: functions.py:783
179 | msgid "Aggregate box plot"
180 | msgstr "Diagramma a scatola aggregato"
181 | 
182 | #: functions.py:835
183 | msgid "unused data"
184 | msgstr "Dati non usati"
185 | 
186 | #: functions.py:839 functions.py:1029
187 | msgid "training data"
188 | msgstr "Dati usati nell'allenamento"
189 | 
190 | #: functions.py:846
191 | msgid "validation data"
192 | msgstr "Dati di validazione"
193 | 
194 | #: functions.py:851
195 | msgid "yhat"
196 | msgstr "Predizione"
197 | 
198 | #: functions.py:855
199 | msgid "yhatlower"
200 | msgstr "Predizione (limite inferiore)"
201 | 
202 | #: functions.py:858
203 | msgid "yhatupper"
204 | msgstr "Predizione (limite superiore)"
205 | 
206 | #: functions.py:862
207 | msgid "Best prediction for the validation set"
208 | msgstr "Miglior previsione per i dati di validazione"
209 | 
210 | #: functions.py:907
211 | msgid "This model, during the history, reached these performances on unseen data:"
212 | msgstr ""
213 | "Questo modello, nella storia della serie, ha raggiunto queste "
214 | "performances su dati non visti:"
215 | 
216 | #: functions.py:909
217 | msgid "Range of validation data: "
218 | msgstr "Range dei dati di validazione: "
219 | 
220 | #: functions.py:915
221 | msgid "real data"
222 | msgstr "Dati reali"
223 | 
224 | #: functions.py:920
225 | msgid "historical prediction"
226 | msgstr "Previsione storica"
227 | 
228 | #: functions.py:928
229 | msgid "future yhat"
230 | msgstr "Previsione futura"
231 | 
232 | #: functions.py:934 functions.py:937
233 | msgid "Error series"
234 | msgstr "Errore"
235 | 
236 | #: functions.py:936 functions.py:940
237 | msgid "Historical prediction"
238 | msgstr "Previsione storica"
239 | 
240 | #: functions.py:949 functions.py:957
241 | msgid "Error series histogram"
242 | msgstr "Istogramma dell'errore"
243 | 
244 | #: functions.py:1042
245 | msgid "Performances with different training windows"
246 | msgstr "Performances con differenti finestre di allenamento"
247 | 
248 | #: functions.py:1090
249 | msgid "Model type: "
250 | msgstr "Classe del modello: "
251 | 
252 | #: functions.py:1091
253 | #, fuzzy
254 | msgid "Values used for validation: last "
255 | msgstr "Valori usati per il testing: ultimi "
256 | 
257 | #: functions.py:1091
258 | msgid " values"
259 | msgstr " valori"
260 | 
261 | #: functions.py:1092
262 | msgid "The length of the training windows is the "
263 | msgstr "La lunghezza della finestra di addestramento è il "
264 | 
265 | #: functions.py:1093
266 | msgid " of the length of the time series."
267 | msgstr " della lunghezza della serie temporale."
268 | 
269 | #: functions.py:1094
270 | msgid "Training windows are composed of "
271 | msgstr "Le finestre di allenamento sono composte del "
272 | 
273 | #: functions.py:1094
274 | msgid " values."
275 | msgstr " valori."
276 | 
277 | #: functions.py:1095
278 | msgid "The model has used "
279 | msgstr "Il modello ha usato "
280 | 
281 | #: functions.py:1095
282 | msgid " as extra-regressor(s) to improve the training."
283 | msgstr " come regressore/i extra per migliorare l'addestramento."
284 | 
285 | #: functions.py:1096
286 | msgid "The model has used a "
287 | msgstr "Il modello ha usato una "
288 | 
289 | #: functions.py:1096
290 | msgid " transformation on the input data."
291 | msgstr " trasformazione sui dati."
292 | 
293 | #: functions.py:1098
294 | msgid "The model has not used any pre/post transformation on input data."
295 | msgstr "Il modello non ha usato pre/post trasformazioni sui dati."
296 | 
297 | #: functions.py:1102
298 | msgid "Model characteristics:"
299 | msgstr "Caratteristiche del modello:"
300 | 
301 | #: functions.py:1104
302 | msgid "This model, using the best training window, reaches these performances:"
303 | msgstr ""
304 | "Questo modello, usando la miglior finestra di allenamento, raggiunge "
305 | "queste performances:"
306 | 
307 | #: functions.py:1139
308 | msgid "Arithmetic mean of errors:"
309 | msgstr "Media aritmetica dell'errore: "
310 | 
311 | #: functions.py:1140
312 | msgid "Standard deviation of errors: "
313 | msgstr "Deviazione standard dell'errore: "
314 | 
315 | 


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