├── .gitignore
├── Dockerfile
├── Makefile
├── README.md
├── autoformer_multivariate.py
├── environment.yml
├── evaluation.py
├── images
├── nhits-arch.png
└── results.png
├── long_horizon_baselines.ipynb
├── nhits_intuition.ipynb
├── nhits_multivariate.py
├── residuals.py
├── rnn_multivariate.py
└── src
├── __init__.py
├── data
├── __init__.py
├── datasets
│ ├── __init__.py
│ ├── ecl.py
│ ├── epf.py
│ ├── ett.py
│ ├── favorita.py
│ ├── gefcom2012.py
│ ├── gefcom2014.py
│ ├── m3.py
│ ├── m4.py
│ ├── m5.py
│ ├── tourism.py
│ ├── utils.py
│ └── wth.py
├── scalers.py
├── tsdataset.py
├── tsloader.py
└── utils.py
├── experiments
├── __init__.py
└── utils.py
├── losses
├── __init__.py
├── numpy.py
├── pytorch.py
└── utils.py
└── models
├── __init__.py
├── components
├── __init__.py
├── autocorrelation.py
├── autoformer.py
├── common.py
├── drnn.py
├── embed.py
├── selfattention.py
├── tcn.py
└── transformer.py
├── esrnn
├── __init__.py
└── esrnn.py
├── nbeats
├── __init__.py
├── ensemble.py
└── nbeats.py
├── nhits
├── __init__.py
└── nhits.py
└── transformer
├── __init__.py
├── autoformer.py
├── informer.py
└── transformer.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 | *.DS_Store
9 |
10 | # Distribution / packaging
11 | .Python
12 | build/
13 | develop-eggs/
14 | dist/
15 | downloads/
16 | eggs/
17 | .eggs/
18 | lib/
19 | lib64/
20 | parts/
21 | sdist/
22 | var/
23 | wheels/
24 | pip-wheel-metadata/
25 | share/python-wheels/
26 | *.egg-info/
27 | .installed.cfg
28 | *.egg
29 | MANIFEST
30 |
31 | # PyInstaller
32 | # Usually these files are written by a python script from a template
33 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
34 | *.manifest
35 | *.spec
36 |
37 | # Installer logs
38 | pip-log.txt
39 | pip-delete-this-directory.txt
40 |
41 | # Unit test / coverage reports
42 | htmlcov/
43 | .tox/
44 | .nox/
45 | .coverage
46 | .coverage.*
47 | .cache
48 | nosetests.xml
49 | coverage.xml
50 | *.cover
51 | *.py,cover
52 | .hypothesis/
53 | .pytest_cache/
54 |
55 | # Translations
56 | *.mo
57 | *.pot
58 |
59 | # Django stuff:
60 | *.log
61 | local_settings.py
62 | db.sqlite3
63 | db.sqlite3-journal
64 |
65 | # Flask stuff:
66 | instance/
67 | .webassets-cache
68 |
69 | # Scrapy stuff:
70 | .scrapy
71 |
72 | # Sphinx documentation
73 | docs/_build/
74 |
75 | # PyBuilder
76 | target/
77 |
78 | # Jupyter Notebook
79 | .ipynb_checkpoints
80 |
81 | # IPython
82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | .python-version
87 |
88 | # pipenv
89 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
90 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
91 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
92 | # install all needed dependencies.
93 | #Pipfile.lock
94 |
95 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
96 | __pypackages__/
97 |
98 | # Celery stuff
99 | celerybeat-schedule
100 | celerybeat.pid
101 |
102 | # SageMath parsed files
103 | *.sage.py
104 |
105 | # Environments
106 | .env
107 | .venv
108 | env/
109 | venv/
110 | ENV/
111 | env.bak/
112 | venv.bak/
113 |
114 | # Spyder project settings
115 | .spyderproject
116 | .spyproject
117 |
118 | # Rope project settings
119 | .ropeproject
120 |
121 | # mkdocs documentation
122 | /site
123 |
124 | # mypy
125 | .mypy_cache/
126 | .dmypy.json
127 | dmypy.json
128 |
129 | # Pyre type checker
130 | .pyre/
131 |
132 | # data
133 | results/
134 | data/
135 | *.csv
136 |
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/Dockerfile:
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1 | FROM nvidia/cuda:11.2.0-devel-ubuntu18.04
2 |
3 | ENV DEBIAN_FRONTEND=noninteractive
4 |
5 | RUN apt-get update -y --fix-missing && \
6 | apt-get install -y --no-install-recommends \
7 | software-properties-common \
8 | wget \
9 | curl \
10 | unrar \
11 | unzip \
12 | git && \
13 | apt-get upgrade -y libstdc++6 && \
14 | apt-get clean -y
15 |
16 | RUN add-apt-repository ppa:ubuntu-toolchain-r/test && \
17 | apt-get update && \
18 | apt-get install -y gcc-9 && \
19 | apt-get upgrade -y libstdc++6
20 |
21 | RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
22 | bash Miniconda3-latest-Linux-x86_64.sh -p /miniconda -b && \
23 | rm -rf Miniconda3-latest-Linux-x86_64.sh
24 |
25 | ENV PATH=/miniconda/bin:${PATH}
26 | RUN conda update -y conda
27 |
28 | RUN conda install -n base -c conda-forge mamba
29 |
30 | ADD ./environment.yml ./environment.yml
31 | RUN mamba env update -n base -f ./environment.yml && \
32 | conda clean -afy
33 |
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/Makefile:
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1 | IMAGE := nhits
2 | ROOT := $(shell dirname $(realpath $(firstword ${MAKEFILE_LIST})))
3 | PARENT_ROOT := $(shell dirname ${ROOT})
4 | PORT := 8888
5 |
6 | DOCKER_PARAMETERS := \
7 | --user $(shell id -u) \
8 | -v ${ROOT}:/app \
9 | -w /app \
10 | -e HOME=/tmp
11 |
12 | ifdef gpu
13 | DOCKER_PARAMETERS += --gpus all
14 | endif
15 |
16 | init:
17 | docker build -t ${IMAGE} .
18 |
19 | get_dataset:
20 | $(MAKE) run_module module="mkdir -p data/"
21 | $(MAKE) run_module module="wget -O data/datasets.zip https://nhits-experiments.s3.amazonaws.com/datasets.zip"
22 | $(MAKE) run_module module="unzip data/datasets.zip -d data/"
23 |
24 | jupyter:
25 | docker run -d --rm ${DOCKER_PARAMETERS} -e HOME=/tmp -p ${PORT}:8888 ${IMAGE} \
26 | bash -c "jupyter lab --ip=0.0.0.0 --no-browser --NotebookApp.token=''"
27 |
28 | run_module: .require-module
29 | docker run -i --rm ${DOCKER_PARAMETERS} \
30 | ${IMAGE} ${module}
31 |
32 | bash_docker:
33 | docker run -it --rm ${DOCKER_PARAMETERS} ${IMAGE}
34 |
35 | .require-module:
36 | ifndef module
37 | $(error module is required)
38 | endif
39 |
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/README.md:
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1 | # N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
2 |
3 | Recent progress in neural forecasting instigated significant improvements in the accuracy of large-scale forecasting systems. Yet, extremely long horizon forecasting remains a very difficult task. Two common challenges afflicting the long horizon forecasting are the volatility of the predictions and their computational complexity. In this paper we introduce `N-HiTS`, which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable our method to assemble its predictions sequentially, selectively emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We conduct an extensive empirical evaluation demonstrating the advantages of `N-HiTS` over the state-of-the-art long-horizon forecasting methods. On an array of multivariate forecasting tasks, our method provides an average accuracy improvement of 25% over the latest Transformer architectures while reducing the computational time by orders of magnitude.
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