├── LICENSE ├── README.md ├── notebooks ├── 00-download-m5-data.ipynb ├── 01-make-base-dataset.ipynb ├── 02-feature-engineering.ipynb ├── 03-train-model.ipynb ├── 04-make-forecast.ipynb ├── 05-plots-and-diagnostics.ipynb └── images │ └── recursive_forecasting │ ├── Slide1.png │ ├── Slide2.png │ ├── Slide3.png │ └── Slide4.png └── requirements.txt /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Forecasting the M5 dataset using machine learning 2 | 3 | An end-to-end tutorial on using a global forecasting model (i.e., LightGBM) on a retail sales dataset (the M5 competition) using multi-step recursive forecasting. 4 | 5 | This tutorial emulates a batch forecasting workflow, breaking the process into multiple steps: 6 | 7 | 0. Obtain the raw data. 8 | 1. Create the base dataset containing sales, price, promos etc. by product id 9 | and date. 10 | 2. Create a feature engineering pipeline, create the training data, and store 11 | the pipeline and training data. 12 | 3. Train the model and store it. 13 | 4. Create a forecast using recursive forecasting. 14 | 5. Plot the forecast, feature importance, and other model diagnostics. 15 | 16 | For more on feature engineering for time series forecasting check out this [course](https://www.courses.trainindata.com/p/feature-engineering-for-forecasting). 17 | 18 | # Installation 19 | 20 | This tutorial requires: 21 | - numpy 22 | - pandas 23 | - scikit-learn 24 | - joblib 25 | - matplotlib 26 | - sktime 27 | - jupyter 28 | - pyarrow 29 | - lightgbm 30 | 31 | These can be installed by from the `requirements.txt`. 32 | 33 | ```Python 34 | pip install -r requirements.txt 35 | ``` 36 | 37 | The notebooks were run on Python 3.10.2. 38 | -------------------------------------------------------------------------------- /notebooks/00-download-m5-data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "60c71558-8af4-403c-8712-ee8974a22412", 6 | "metadata": {}, 7 | "source": [ 8 | "# Step 0: Download the M5 Kaggle dataset" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "b2333b7d-b922-4f9d-8d7d-2191f9bf194f", 14 | "metadata": {}, 15 | "source": [ 16 | "In this notebook we provide instructions to download the data and where to place it." 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "id": "15f7187d-2c7f-4301-a63a-bfc7c856e315", 22 | "metadata": {}, 23 | "source": [ 24 | "We will create a directory called `data_sources`, it will be in the same location as these notebooks. We will place the csv files from Kaggle here." 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 1, 30 | "id": "ac614a03-0b02-42b7-abbf-ac306cdf1949", 31 | "metadata": { 32 | "tags": [] 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "from pathlib import Path\n", 37 | "data_path = Path(\"../data_sources\")\n", 38 | "data_path.mkdir(exist_ok=True) # Make the `data_sources` directory" 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "id": "c825bf3c-055d-40ef-bb1b-059eb1f80cb9", 44 | "metadata": {}, 45 | "source": [ 46 | "The data can be found here: https://www.kaggle.com/competitions/m5-forecasting-accuracy/data [1].\n", 47 | "\n", 48 | "Download the data via the Kaggle website and store all the csv files (we will only use `calendar.csv`, `sales_train_evaluation.csv`, `sell_prices.csv`) in the `data_sources` directory. The `data_sources` directory should be in the root directory of the repository (i.e., one directory above the `notebooks` directory).\n", 49 | " \n", 50 | "References:\n", 51 | "\n", 52 | "[1] Addison Howard & Spyros Makridakis, M5 Forecasting - Accuracy, Kaggle, 2020, https://kaggle.com/competitions/m5-forecasting-accuracy" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "id": "bd5ba58d-91e1-4a2e-97de-d1f2396c8a4c", 58 | "metadata": {}, 59 | "source": [ 60 | "Let's check that we've put the right files in the right location." 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 2, 66 | "id": "38dd53e0-c0c7-4c65-970a-11ad37ef4644", 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "name": "stdout", 71 | "output_type": "stream", 72 | "text": [ 73 | "../data_sources/sales_train_evaluation.csv exists!\n", 74 | "../data_sources/calendar.csv exists!\n", 75 | "../data_sources/sell_prices.csv exists!\n" 76 | ] 77 | } 78 | ], 79 | "source": [ 80 | "sales_data = data_path / \"sales_train_evaluation.csv\"\n", 81 | "calendar_data = data_path / \"calendar.csv\"\n", 82 | "price_data = data_path / \"sell_prices.csv\"\n", 83 | "\n", 84 | "paths_to_data_sources = [sales_data, calendar_data, price_data]\n", 85 | "\n", 86 | "for path in paths_to_data_sources:\n", 87 | " if path.is_file():\n", 88 | " print(f\"{path} exists!\")\n", 89 | " else:\n", 90 | " print(f\"{path} does not exist! Please add it to {data_path}!\")" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "id": "07fa993d-695a-412d-87ff-852d1fd6eda5", 96 | "metadata": {}, 97 | "source": [ 98 | "Let's move onto the next notebook where we will start transforming the data into something usable." 99 | ] 100 | } 101 | ], 102 | "metadata": { 103 | "kernelspec": { 104 | "display_name": "Python 3 (ipykernel)", 105 | "language": "python", 106 | "name": "python3" 107 | }, 108 | "language_info": { 109 | "codemirror_mode": { 110 | "name": "ipython", 111 | "version": 3 112 | }, 113 | "file_extension": ".py", 114 | "mimetype": "text/x-python", 115 | "name": "python", 116 | "nbconvert_exporter": "python", 117 | "pygments_lexer": "ipython3", 118 | "version": "3.9.10" 119 | }, 120 | "vscode": { 121 | "interpreter": { 122 | "hash": "c353dbc0844cb54de327ff22e9f707bab766d5a7d7ed2a2fa40c42a293e1bad3" 123 | } 124 | } 125 | }, 126 | "nbformat": 4, 127 | "nbformat_minor": 5 128 | } 129 | -------------------------------------------------------------------------------- /notebooks/01-make-base-dataset.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "60c71558-8af4-403c-8712-ee8974a22412", 6 | "metadata": {}, 7 | "source": [ 8 | "# Step 1: Make the base dataset\n", 9 | "\n", 10 | "In this notebook we will create the base dataset that we will work with for feature engineering and modelling. The base dataset is the data after joining all the relevant information we need for feature engineering and modelling (e.g., units sold, price, special events, etc.). The raw data from Kaggle comes as a set of csv files. We will load each csv file and join them to create our base dataset. We will then compute features from this base dataset in later notebooks. \n", 11 | "\n", 12 | "In a business setting the data would likely be stored in a database in separate tables. So one common pattern would be to use SQL or Spark to create the base dataset. If we were producing a regular forecast as a batch process (e.g., a weekly forecast), we would run this step periodically as the source data gets updated." 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "id": "f7e7228c-2754-4bce-8dc2-59c2efb4666f", 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "from pathlib import Path\n", 23 | "\n", 24 | "import numpy as np\n", 25 | "import pandas as pd" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "22f65005-7386-4f48-94ff-312b20a0cc60", 31 | "metadata": {}, 32 | "source": [ 33 | "# Config paths" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "id": "2e9d15a2-a4f9-408e-aa03-6fdd853afadb", 39 | "metadata": {}, 40 | "source": [ 41 | "Specify the directories that we will read data from and write data to. In practice we would store this information in a separate configuration file rather than duplicate it across all notebooks. For simplicity, we specify the paths in the notebook itself." 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 2, 47 | "id": "c34eba65-84e0-4055-9cf5-46d787c1122a", 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "# Directory containing the raw data\n", 52 | "data_sources = Path(\"../data_sources\")\n", 53 | "\n", 54 | "# Output processed data (i.e., the base dataset)\n", 55 | "processed_data_dir = Path(\"../processed_data\")\n", 56 | "processed_data_dir.mkdir(exist_ok=True)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "id": "6d1c3e66-31d1-4a78-9d1e-54f0cb8ea223", 62 | "metadata": {}, 63 | "source": [ 64 | "Specify path to the datasets we will use." 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 3, 70 | "id": "ebb02318-4acb-4792-95b0-1268474cbd86", 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "sales_data = data_sources / \"sales_train_evaluation.csv\"\n", 75 | "calendar_and_promos_data = data_sources / \"calendar.csv\"\n", 76 | "price_data = data_sources / \"sell_prices.csv\"" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "id": "149a97c5-cc12-4736-9ce6-776542667260", 82 | "metadata": {}, 83 | "source": [ 84 | "# Load datasets" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "id": "805d5adb-fcfd-4ac3-8a8b-a1642a48b2b9", 90 | "metadata": {}, 91 | "source": [ 92 | "We will specify `dtypes` of each column and pass this to `pd.read_csv` when loading data. This will allow us to be more memory efficient (e.g., using `category` rather than `object` to denote categorical variables in our pandas dataframes)." 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "id": "905264a6-85aa-46bf-a316-9138934a9907", 98 | "metadata": {}, 99 | "source": [ 100 | "**Load sales data**" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 4, 106 | "id": "cd21efc8-586c-4be0-b6fc-94161bc1d22d", 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "data": { 111 | "text/html": [ 112 | "
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iditem_iddept_idcat_idstore_idstate_idd_1d_2d_3d_4...d_1932d_1933d_1934d_1935d_1936d_1937d_1938d_1939d_1940d_1941
0HOBBIES_1_001_CA_1_evaluationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CA0000...2400003301
1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CA0000...0121100000
2HOBBIES_1_003_CA_1_evaluationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CA0000...1020002301
3HOBBIES_1_004_CA_1_evaluationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CA0000...1104013026
4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CA0000...0002100210
\n", 276 | "

5 rows × 1947 columns

\n", 277 | "
" 278 | ], 279 | "text/plain": [ 280 | " id item_id dept_id cat_id store_id \\\n", 281 | "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", 282 | "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", 283 | "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", 284 | "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", 285 | "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", 286 | "\n", 287 | " state_id d_1 d_2 d_3 d_4 ... d_1932 d_1933 d_1934 d_1935 d_1936 \\\n", 288 | "0 CA 0 0 0 0 ... 2 4 0 0 0 \n", 289 | "1 CA 0 0 0 0 ... 0 1 2 1 1 \n", 290 | "2 CA 0 0 0 0 ... 1 0 2 0 0 \n", 291 | "3 CA 0 0 0 0 ... 1 1 0 4 0 \n", 292 | "4 CA 0 0 0 0 ... 0 0 0 2 1 \n", 293 | "\n", 294 | " d_1937 d_1938 d_1939 d_1940 d_1941 \n", 295 | "0 0 3 3 0 1 \n", 296 | "1 0 0 0 0 0 \n", 297 | "2 0 2 3 0 1 \n", 298 | "3 1 3 0 2 6 \n", 299 | "4 0 0 2 1 0 \n", 300 | "\n", 301 | "[5 rows x 1947 columns]" 302 | ] 303 | }, 304 | "execution_count": 4, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "sales_dtypes = {\n", 311 | " \"id\": \"category\",\n", 312 | " \"item_id\": \"category\",\n", 313 | " \"dept_id\": \"category\",\n", 314 | " \"cat_id\": \"category\",\n", 315 | " \"store_id\": \"category\",\n", 316 | " \"state_id\": \"category\",\n", 317 | " **{f\"d_{i}\": np.uint64 for i in range(1942)},\n", 318 | "}\n", 319 | "\n", 320 | "df = pd.read_csv(sales_data, dtype=sales_dtypes)\n", 321 | "df.head()" 322 | ] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "id": "1c33b1de-2111-4ba2-96f6-716025ef0ac2", 327 | "metadata": {}, 328 | "source": [ 329 | "**Load calendar & promos data**" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": 5, 335 | "id": "86d90f98-33b4-42f6-a58b-eba6e125faf2", 336 | "metadata": {}, 337 | "outputs": [ 338 | { 339 | "data": { 340 | "text/html": [ 341 | "
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datewm_yr_wkweekdaywdaymonthyeardevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WI
02011-01-2911101Saturday112011d_1NaNNaNNaNNaN000
12011-01-3011101Sunday212011d_2NaNNaNNaNNaN000
22011-01-3111101Monday312011d_3NaNNaNNaNNaN000
32011-02-0111101Tuesday422011d_4NaNNaNNaNNaN110
42011-02-0211101Wednesday522011d_5NaNNaNNaNNaN101
\n", 463 | "
" 464 | ], 465 | "text/plain": [ 466 | " date wm_yr_wk weekday wday month year d event_name_1 \\\n", 467 | "0 2011-01-29 11101 Saturday 1 1 2011 d_1 NaN \n", 468 | "1 2011-01-30 11101 Sunday 2 1 2011 d_2 NaN \n", 469 | "2 2011-01-31 11101 Monday 3 1 2011 d_3 NaN \n", 470 | "3 2011-02-01 11101 Tuesday 4 2 2011 d_4 NaN \n", 471 | "4 2011-02-02 11101 Wednesday 5 2 2011 d_5 NaN \n", 472 | "\n", 473 | " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI \n", 474 | "0 NaN NaN NaN 0 0 0 \n", 475 | "1 NaN NaN NaN 0 0 0 \n", 476 | "2 NaN NaN NaN 0 0 0 \n", 477 | "3 NaN NaN NaN 1 1 0 \n", 478 | "4 NaN NaN NaN 1 0 1 " 479 | ] 480 | }, 481 | "execution_count": 5, 482 | "metadata": {}, 483 | "output_type": "execute_result" 484 | } 485 | ], 486 | "source": [ 487 | "cal_dtypes = {\n", 488 | " \"d\": \"category\",\n", 489 | " \"wm_yr_wk\": np.uint16,\n", 490 | " \"event_name_1\": \"category\",\n", 491 | " \"event_type_1\": \"category\",\n", 492 | " \"event_name_2\": \"category\",\n", 493 | " \"event_type_2\": \"category\",\n", 494 | " \"snap_CA\": np.uint8,\n", 495 | " \"snap_TX\": np.uint8,\n", 496 | " \"snap_WI\": np.uint8,\n", 497 | "}\n", 498 | "df_cal = pd.read_csv(calendar_and_promos_data, dtype=cal_dtypes, parse_dates=[\"date\"])\n", 499 | "\n", 500 | "df_cal.head()" 501 | ] 502 | }, 503 | { 504 | "cell_type": "markdown", 505 | "id": "5b7d19a2-ac7b-47b2-8024-0efc22188108", 506 | "metadata": {}, 507 | "source": [ 508 | "The `event` columns represent the absence of an event with a `NaN` value. Missing data will cause issues with feature engineering steps and some ML models. So we will impute the missing values with an additional category called `\"no_event\"`." 509 | ] 510 | }, 511 | { 512 | "cell_type": "code", 513 | "execution_count": 6, 514 | "id": "578da9de-770b-4d72-93c9-099eebace15d", 515 | "metadata": {}, 516 | "outputs": [], 517 | "source": [ 518 | "for event_col in df_cal.filter(like=\"event\").columns:\n", 519 | " df_cal[event_col] = df_cal[event_col].cat.add_categories(\"no_event\").fillna(\"no_event\")" 520 | ] 521 | }, 522 | { 523 | "cell_type": "markdown", 524 | "id": "c23e9f89-675f-48cc-b527-ac7cf9e586a5", 525 | "metadata": {}, 526 | "source": [ 527 | "**Load price data**" 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": 7, 533 | "id": "a6157f72-dcac-4a49-8cf6-721f4c4d4035", 534 | "metadata": {}, 535 | "outputs": [ 536 | { 537 | "data": { 538 | "text/html": [ 539 | "
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store_iditem_idwm_yr_wksell_price
0CA_1HOBBIES_1_001113259.58
1CA_1HOBBIES_1_001113269.58
2CA_1HOBBIES_1_001113278.26
3CA_1HOBBIES_1_001113288.26
4CA_1HOBBIES_1_001113298.26
\n", 601 | "
" 602 | ], 603 | "text/plain": [ 604 | " store_id item_id wm_yr_wk sell_price\n", 605 | "0 CA_1 HOBBIES_1_001 11325 9.58\n", 606 | "1 CA_1 HOBBIES_1_001 11326 9.58\n", 607 | "2 CA_1 HOBBIES_1_001 11327 8.26\n", 608 | "3 CA_1 HOBBIES_1_001 11328 8.26\n", 609 | "4 CA_1 HOBBIES_1_001 11329 8.26" 610 | ] 611 | }, 612 | "execution_count": 7, 613 | "metadata": {}, 614 | "output_type": "execute_result" 615 | } 616 | ], 617 | "source": [ 618 | "price_dtypes = {\n", 619 | " \"store_id\": \"category\",\n", 620 | " \"item_id\": \"category\",\n", 621 | " \"wm_yr_wk\": np.uint16,\n", 622 | " \"sell_price\": np.float64,\n", 623 | "}\n", 624 | "df_price = pd.read_csv(price_data, dtype=price_dtypes)\n", 625 | "\n", 626 | "df_price.head()" 627 | ] 628 | }, 629 | { 630 | "cell_type": "markdown", 631 | "id": "67cd1f64-a514-44a2-97a6-494904c4e044", 632 | "metadata": {}, 633 | "source": [ 634 | "# Create a panel view of the time series" 635 | ] 636 | }, 637 | { 638 | "cell_type": "markdown", 639 | "id": "c653fee0-6c99-44aa-974c-5b9f88c053ea", 640 | "metadata": {}, 641 | "source": [ 642 | "Create a panel view of the time series using `melt`." 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 8, 648 | "id": "8825b029-9cfc-4d72-bfed-a42fd5bd8f7c", 649 | "metadata": {}, 650 | "outputs": [ 651 | { 652 | "data": { 653 | "text/html": [ 654 | "
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iditem_iddept_idcat_idstore_idstate_iddy
0HOBBIES_1_001_CA_1_evaluationHOBBIES_1_001HOBBIES_1HOBBIESCA_1CAd_10
1HOBBIES_1_002_CA_1_evaluationHOBBIES_1_002HOBBIES_1HOBBIESCA_1CAd_10
2HOBBIES_1_003_CA_1_evaluationHOBBIES_1_003HOBBIES_1HOBBIESCA_1CAd_10
3HOBBIES_1_004_CA_1_evaluationHOBBIES_1_004HOBBIES_1HOBBIESCA_1CAd_10
4HOBBIES_1_005_CA_1_evaluationHOBBIES_1_005HOBBIES_1HOBBIESCA_1CAd_10
\n", 740 | "
" 741 | ], 742 | "text/plain": [ 743 | " id item_id dept_id cat_id store_id \\\n", 744 | "0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", 745 | "1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", 746 | "2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", 747 | "3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", 748 | "4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", 749 | "\n", 750 | " state_id d y \n", 751 | "0 CA d_1 0 \n", 752 | "1 CA d_1 0 \n", 753 | "2 CA d_1 0 \n", 754 | "3 CA d_1 0 \n", 755 | "4 CA d_1 0 " 756 | ] 757 | }, 758 | "execution_count": 8, 759 | "metadata": {}, 760 | "output_type": "execute_result" 761 | } 762 | ], 763 | "source": [ 764 | "df_sales = df.melt(\n", 765 | " id_vars=[\"id\", \"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"],\n", 766 | " var_name=\"d\",\n", 767 | " value_name=\"y\", # `y` is the number of units sold on a given day\n", 768 | ")\n", 769 | "\n", 770 | "df_sales.head()" 771 | ] 772 | }, 773 | { 774 | "cell_type": "markdown", 775 | "id": "0d543ed5-5d8e-47c8-8030-31a5b3fc58b9", 776 | "metadata": {}, 777 | "source": [ 778 | "# Create base data set by joining date, special events, and promos columns to sales dataframe." 779 | ] 780 | }, 781 | { 782 | "cell_type": "markdown", 783 | "id": "301b8678-f799-472e-b39a-775ed1a46ce8", 784 | "metadata": {}, 785 | "source": [ 786 | "Join calendar data to sales dataframe." 787 | ] 788 | }, 789 | { 790 | "cell_type": "code", 791 | "execution_count": 9, 792 | "id": "9f2f48cf-5b2e-47d0-9cd9-d623a3a8a4a5", 793 | "metadata": {}, 794 | "outputs": [], 795 | "source": [ 796 | "df_sales = df_sales.merge(\n", 797 | " right=df_cal[\n", 798 | " [\n", 799 | " \"d\",\n", 800 | " \"date\",\n", 801 | " \"wm_yr_wk\",\n", 802 | " \"event_name_1\",\n", 803 | " \"event_type_1\",\n", 804 | " \"event_name_2\",\n", 805 | " \"event_type_2\",\n", 806 | " \"snap_CA\",\n", 807 | " \"snap_TX\",\n", 808 | " \"snap_WI\",\n", 809 | " ]\n", 810 | " ],\n", 811 | " on=\"d\",\n", 812 | ")" 813 | ] 814 | }, 815 | { 816 | "cell_type": "markdown", 817 | "id": "60280278-cd41-4efe-b669-40a8592f33de", 818 | "metadata": {}, 819 | "source": [ 820 | "Join price column to sales dataframe." 821 | ] 822 | }, 823 | { 824 | "cell_type": "code", 825 | "execution_count": null, 826 | "id": "0607b24d-0aed-4d8c-a33a-505757696b35", 827 | "metadata": {}, 828 | "outputs": [], 829 | "source": [ 830 | "df_sales = df_sales.merge(right=df_price, on=[\"store_id\", \"item_id\", \"wm_yr_wk\"])" 831 | ] 832 | }, 833 | { 834 | "cell_type": "markdown", 835 | "id": "081d14c4-e3e6-4919-bd95-9e78badc05c2", 836 | "metadata": {}, 837 | "source": [ 838 | "Sort by time series id and date, and drop columns that are not needed. " 839 | ] 840 | }, 841 | { 842 | "cell_type": "code", 843 | "execution_count": null, 844 | "id": "436c7ee7-9daf-45a9-9dc0-7fea45859c32", 845 | "metadata": {}, 846 | "outputs": [], 847 | "source": [ 848 | "df_sales =(\n", 849 | " df_sales.sort_values(by=[\"id\", \"date\"]) # Sort by time series id and date.\n", 850 | " .drop(columns=[\"wm_yr_wk\", \"d\"]) # Drop columns that are no longer needed.\n", 851 | " )" 852 | ] 853 | }, 854 | { 855 | "cell_type": "markdown", 856 | "id": "07eaa3d1-386d-4b58-b15e-c1cdb97dba5a", 857 | "metadata": {}, 858 | "source": [ 859 | "df_sales.head()\n", 860 | "Let's look at the final dataset." 861 | ] 862 | }, 863 | { 864 | "cell_type": "code", 865 | "execution_count": null, 866 | "id": "aaa4e2aa-bd46-4f39-9a21-2518fc36ff27", 867 | "metadata": {}, 868 | "outputs": [], 869 | "source": [ 870 | "df_sales.head()\n" 871 | ] 872 | }, 873 | { 874 | "cell_type": "code", 875 | "execution_count": null, 876 | "id": "b097f535-5320-4baf-b66c-dd3cc557ef6d", 877 | "metadata": {}, 878 | "outputs": [], 879 | "source": [ 880 | "display(df_sales.shape)" 881 | ] 882 | }, 883 | { 884 | "cell_type": "markdown", 885 | "id": "4a6d4ad6-fb6a-449d-9559-1f729ed80f74", 886 | "metadata": {}, 887 | "source": [ 888 | "Let's save the full dataset as parquet (far more memory efficient than csv). In this example, we will partition the data by `store_id` so we can make it easier to read the data of just one store. \n", 889 | "\n", 890 | "See these links to learn more about working with the parquet file format: \n", 891 | "* https://parquet.apache.org/docs/\n", 892 | "* https://towardsdatascience.com/easy-parquet-tutorial-best-practices-237955e46cb7\n", 893 | "* https://blog.datasyndrome.com/python-and-parquet-performance-e71da65269ce" 894 | ] 895 | }, 896 | { 897 | "cell_type": "code", 898 | "execution_count": null, 899 | "id": "5d3ccb2d-7534-4644-8992-1b81c799a35d", 900 | "metadata": {}, 901 | "outputs": [], 902 | "source": [ 903 | "f_out = processed_data_dir / \"data\" # We specify a directory rather than a file when\n", 904 | " # saving partitioned data.\n", 905 | "df_sales.to_parquet(f_out, \n", 906 | " index=False, \n", 907 | " engine=\"pyarrow\", \n", 908 | " partition_cols=[\"store_id\"]\n", 909 | " )" 910 | ] 911 | }, 912 | { 913 | "cell_type": "markdown", 914 | "id": "636259e6-c2c2-47ae-bd46-b140eaff60d1", 915 | "metadata": {}, 916 | "source": [ 917 | "# An aside on row groups" 918 | ] 919 | }, 920 | { 921 | "cell_type": "markdown", 922 | "id": "ed653b03-d763-4c69-90b0-19503870c515", 923 | "metadata": {}, 924 | "source": [ 925 | "See the below article on predicate pushdown filtering and row groups. This allows us to apply filters before the data is read into memory, which is pretty neat!\n", 926 | "\n", 927 | "http://peter-hoffmann.com/2020/understand-predicate-pushdown-on-rowgroup-level-in-parquet-with-pyarrow-and-python.html" 928 | ] 929 | }, 930 | { 931 | "cell_type": "code", 932 | "execution_count": null, 933 | "id": "eb28ff50-356e-4c42-8677-35c5e061989f", 934 | "metadata": {}, 935 | "outputs": [], 936 | "source": [ 937 | "f_in = processed_data_dir / \"data\"\n", 938 | "df = pd.read_parquet(\n", 939 | " path=f_in, \n", 940 | " engine=\"pyarrow\",\n", 941 | " filters=[\n", 942 | " (\"store_id\", \"=\", \"CA_1\"), # Only load this partition.\n", 943 | " (\"date\", \">=\", pd.to_datetime(\"2012-01-01\")) # Filter on date if\n", 944 | " # desired.\n", 945 | " ] \n", 946 | ")" 947 | ] 948 | }, 949 | { 950 | "cell_type": "code", 951 | "execution_count": null, 952 | "id": "6bc91f45-3775-4000-92a2-d37190d02627", 953 | "metadata": {}, 954 | "outputs": [], 955 | "source": [ 956 | "import pyarrow.parquet as pq" 957 | ] 958 | }, 959 | { 960 | "cell_type": "markdown", 961 | "id": "9b4cffdf-028b-40fd-9b7b-0ca150ec9b3e", 962 | "metadata": {}, 963 | "source": [ 964 | "Let's look at a specific parquet file in our partition." 965 | ] 966 | }, 967 | { 968 | "cell_type": "code", 969 | "execution_count": null, 970 | "id": "d11bd08f-d6f8-4eb6-93cc-9049e4d035ec", 971 | "metadata": {}, 972 | "outputs": [], 973 | "source": [ 974 | "pq_file = pq.ParquetFile(f_out / \"store_id=CA_1\" / \"e77d9d89c6c5408896fa2d767a1e9a6d-0.parquet\")" 975 | ] 976 | }, 977 | { 978 | "cell_type": "code", 979 | "execution_count": null, 980 | "id": "62d239a5-e192-4365-b135-e4a8d78901ee", 981 | "metadata": {}, 982 | "outputs": [], 983 | "source": [ 984 | "display([\"columns:\", pq_file.metadata.num_columns],\n", 985 | " [\"rows:\", pq_file.metadata.num_rows],\n", 986 | " [\"row_roups:\", pq_file.metadata.num_row_groups])" 987 | ] 988 | }, 989 | { 990 | "cell_type": "code", 991 | "execution_count": null, 992 | "id": "6a2e6c1a-9e8d-4397-84b6-00f35931d870", 993 | "metadata": {}, 994 | "outputs": [], 995 | "source": [ 996 | "pq_file.metadata.row_group(0)" 997 | ] 998 | }, 999 | { 1000 | "cell_type": "code", 1001 | "execution_count": null, 1002 | "id": "2f5fbadb-e440-4838-9f45-b036db22389f", 1003 | "metadata": {}, 1004 | "outputs": [], 1005 | "source": [ 1006 | "pq_file.read_row_group(0).to_pandas()" 1007 | ] 1008 | }, 1009 | { 1010 | "cell_type": "code", 1011 | "execution_count": null, 1012 | "id": "c27835e7-79e1-4430-8b4e-651b70f10f88", 1013 | "metadata": {}, 1014 | "outputs": [], 1015 | "source": [ 1016 | "pq_file.metadata.row_group(0).column(6)" 1017 | ] 1018 | } 1019 | ], 1020 | "metadata": { 1021 | "kernelspec": { 1022 | "display_name": "Python 3 (ipykernel)", 1023 | "language": "python", 1024 | "name": "python3" 1025 | }, 1026 | "language_info": { 1027 | "codemirror_mode": { 1028 | "name": "ipython", 1029 | "version": 3 1030 | }, 1031 | "file_extension": ".py", 1032 | "mimetype": "text/x-python", 1033 | "name": "python", 1034 | "nbconvert_exporter": "python", 1035 | "pygments_lexer": "ipython3", 1036 | "version": "3.9.10" 1037 | }, 1038 | "vscode": { 1039 | "interpreter": { 1040 | "hash": "e4d3367f26284d1c360f4b08d0535a5ca6c77b5fead82e39e12bf29ce18c45ee" 1041 | } 1042 | } 1043 | }, 1044 | "nbformat": 4, 1045 | "nbformat_minor": 5 1046 | } 1047 | -------------------------------------------------------------------------------- /notebooks/04-make-forecast.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "3e6ac208-958c-4ab4-911b-164c21ef2676", 6 | "metadata": {}, 7 | "source": [ 8 | "# Step 4: Make the forecast\n", 9 | "\n", 10 | "In this notebook we will prepare the dataset needed to create the forecast. We will build the multi-step forecast using recursive forecasting." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "id": "f7e7228c-2754-4bce-8dc2-59c2efb4666f", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "import datetime\n", 21 | "from pathlib import Path\n", 22 | "\n", 23 | "import joblib\n", 24 | "import numpy as np\n", 25 | "import pandas as pd\n", 26 | "\n", 27 | "from sklearn import set_config\n", 28 | "set_config(transform_output=\"pandas\")" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "id": "55fcb7dd-d5c2-45ae-9326-dccd018cc7db", 34 | "metadata": {}, 35 | "source": [ 36 | "# Config paths" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "id": "777d73cc-5987-4555-9321-3ca7555ad177", 42 | "metadata": {}, 43 | "source": [ 44 | "Specify the paths and directories from which we will read and write data to. In practice we would store this in a separate file rather than duplicate it across all notebooks. For simplicity, we specify the paths in the notebook itself." 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 2, 50 | "id": "ae932d58-17e7-4e8b-902c-fe01ba63bb70", 51 | "metadata": { 52 | "tags": [] 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "# Directory containing the raw data\n", 57 | "data_sources = Path(\"../data_sources\")\n", 58 | "\n", 59 | "# Output processed data (i.e., the base dataset)\n", 60 | "processed_data_dir = Path(\"../processed_data\")\n", 61 | "\n", 62 | "# Artifacts directory for storing the\n", 63 | "# training data, models, pipelines etc.\n", 64 | "artifacts_dir = Path(\"../artifacts\")\n", 65 | "training_dir = artifacts_dir / \"training\"\n", 66 | "pipeline_dir = artifacts_dir / \"pipeline\"\n", 67 | "model_dir = artifacts_dir / \"model\"\n", 68 | "forecast_dir = artifacts_dir / \"forecast\"" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "id": "4e03a8c8-281e-47fc-ba39-31813997dd33", 74 | "metadata": {}, 75 | "source": [ 76 | "# Load model" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 3, 82 | "id": "0ceb57f4-86c7-46d6-8ff1-d978f98a608b", 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "model = joblib.load(model_dir / \"model.joblib\")" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "id": "18df7058-fbb4-4e19-8873-240e6e05e6b7", 92 | "metadata": {}, 93 | "source": [ 94 | "# Load feature engineering pipeline" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "id": "80632e29-b627-4a19-b6d4-e7a918902217", 100 | "metadata": {}, 101 | "source": [ 102 | "Ensure that we have run (present at the start of the notebook): \n", 103 | " \n", 104 | "```Python\n", 105 | "from sklearn import set_config\n", 106 | "set_config(transform_output=\"pandas\")\n", 107 | "```\n", 108 | "\n", 109 | "Otherwise, the pipeline may output a numpy array rather than a pandas dataframe." 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 4, 115 | "id": "1d0fcc7e-926f-425e-9321-6fe4a2abf77a", 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "pipeline = joblib.load(pipeline_dir/\"pipeline.joblib\")" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "id": "fe719225-a2b6-4e1b-8ead-2a8894ab07ac", 125 | "metadata": {}, 126 | "source": [ 127 | "# Set forecast parameters" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 5, 133 | "id": "b1b7ba96-c386-47be-8c82-6162f629f75b", 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "forecast_start_date = pd.to_datetime(\"2016-05-23\")\n", 138 | "num_steps = 14\n", 139 | "forecast_horizon = pd.date_range(start=forecast_start_date, \n", 140 | " periods=num_steps, \n", 141 | " freq=\"D\")\n", 142 | "\n", 143 | "# Specify how much data before `forecast_start_date` is required\n", 144 | "# for feature engineering.\n", 145 | "look_back_window_size = np.timedelta64(31, \"D\")" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "id": "8a52518b-aabe-4bd8-b09d-71b21a6532a6", 151 | "metadata": {}, 152 | "source": [ 153 | "# Create the predict dataset" 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "id": "ffa3d321-40a7-45ff-9fc7-e6e8aa26c61a", 159 | "metadata": {}, 160 | "source": [ 161 | "We're preparing a dataframe to enable us to implement the following loop." 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "id": "3bc14c69-1467-4b14-b041-8431a4aab7bb", 167 | "metadata": {}, 168 | "source": [ 169 | "![](./images/recursive_forecasting/Slide1.png)\n", 170 | "![](./images/recursive_forecasting/Slide2.png)\n", 171 | "![](./images/recursive_forecasting/Slide3.png)\n", 172 | "![](./images/recursive_forecasting/Slide4.png)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "id": "5fefe523-7b25-460a-8cf7-7aa4d4a2b2ea", 178 | "metadata": {}, 179 | "source": [ 180 | "Query a subset of base dataset and prepare future values." 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 6, 186 | "id": "d3d1bbbe-6c99-444b-b6e1-8260feb4dec8", 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "data": { 191 | "text/html": [ 192 | "
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item_iddept_idcat_idstate_idyevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIsell_pricestore_id
iddate
HOBBIES_1_001_CA_1_evaluation2016-04-22HOBBIES_1_001HOBBIES_1HOBBIESCA0no_eventno_eventno_eventno_event0008.38CA_1
2016-04-23HOBBIES_1_001HOBBIES_1HOBBIESCA1no_eventno_eventno_eventno_event0008.38CA_1
2016-04-24HOBBIES_1_001HOBBIES_1HOBBIESCA1no_eventno_eventno_eventno_event0008.38CA_1
2016-04-25HOBBIES_1_001HOBBIES_1HOBBIESCA0no_eventno_eventno_eventno_event0008.38CA_1
2016-04-26HOBBIES_1_001HOBBIES_1HOBBIESCA0no_eventno_eventno_eventno_event0008.38CA_1
\n", 334 | "
" 335 | ], 336 | "text/plain": [ 337 | " item_id dept_id cat_id \\\n", 338 | "id date \n", 339 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 HOBBIES_1_001 HOBBIES_1 HOBBIES \n", 340 | " 2016-04-23 HOBBIES_1_001 HOBBIES_1 HOBBIES \n", 341 | " 2016-04-24 HOBBIES_1_001 HOBBIES_1 HOBBIES \n", 342 | " 2016-04-25 HOBBIES_1_001 HOBBIES_1 HOBBIES \n", 343 | " 2016-04-26 HOBBIES_1_001 HOBBIES_1 HOBBIES \n", 344 | "\n", 345 | " state_id y event_name_1 \\\n", 346 | "id date \n", 347 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 CA 0 no_event \n", 348 | " 2016-04-23 CA 1 no_event \n", 349 | " 2016-04-24 CA 1 no_event \n", 350 | " 2016-04-25 CA 0 no_event \n", 351 | " 2016-04-26 CA 0 no_event \n", 352 | "\n", 353 | " event_type_1 event_name_2 \\\n", 354 | "id date \n", 355 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 no_event no_event \n", 356 | " 2016-04-23 no_event no_event \n", 357 | " 2016-04-24 no_event no_event \n", 358 | " 2016-04-25 no_event no_event \n", 359 | " 2016-04-26 no_event no_event \n", 360 | "\n", 361 | " event_type_2 snap_CA snap_TX \\\n", 362 | "id date \n", 363 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 no_event 0 0 \n", 364 | " 2016-04-23 no_event 0 0 \n", 365 | " 2016-04-24 no_event 0 0 \n", 366 | " 2016-04-25 no_event 0 0 \n", 367 | " 2016-04-26 no_event 0 0 \n", 368 | "\n", 369 | " snap_WI sell_price store_id \n", 370 | "id date \n", 371 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0 8.38 CA_1 \n", 372 | " 2016-04-23 0 8.38 CA_1 \n", 373 | " 2016-04-24 0 8.38 CA_1 \n", 374 | " 2016-04-25 0 8.38 CA_1 \n", 375 | " 2016-04-26 0 8.38 CA_1 " 376 | ] 377 | }, 378 | "execution_count": 6, 379 | "metadata": {}, 380 | "output_type": "execute_result" 381 | } 382 | ], 383 | "source": [ 384 | "# Read the base data set of just one store.\n", 385 | "# Read only the time period needed.\n", 386 | "f_in = processed_data_dir / \"data\"\n", 387 | "df = pd.read_parquet(\n", 388 | " path=f_in, \n", 389 | " engine=\"pyarrow\", \n", 390 | " filters=[(\"store_id\", \"=\", \"CA_1\"),\n", 391 | " (\"date\", \">=\", forecast_start_date-look_back_window_size), \n", 392 | " (\"date\", \"<\", forecast_start_date ) \n", 393 | " ]\n", 394 | ")\n", 395 | "\n", 396 | "\n", 397 | "# Remove unused categories.\n", 398 | "df[\"id\"] = df[\"id\"].cat.remove_unused_categories()\n", 399 | "\n", 400 | "# Set time series ID and date as index.\n", 401 | "# This is needed for some sktime transformers.\n", 402 | "df = df.set_index([\"id\", \"date\"]).sort_index()\n", 403 | "df.head()" 404 | ] 405 | }, 406 | { 407 | "cell_type": "markdown", 408 | "id": "621dfe3c-e7ed-4863-ab7b-718225ff0c02", 409 | "metadata": {}, 410 | "source": [ 411 | "Create the predict dataset by extending the index and filling in future values. We will naively project the last value carried forward for each non-null column." 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 7, 417 | "id": "cea99f4a-d9c1-440f-830f-fadabb7112c1", 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [ 421 | "predict_data_time = pd.date_range(\n", 422 | " start=forecast_start_date - look_back_window_size,\n", 423 | " end=forecast_horizon[-1],\n", 424 | " freq=\"D\",\n", 425 | ")" 426 | ] 427 | }, 428 | { 429 | "cell_type": "code", 430 | "execution_count": 8, 431 | "id": "c9ba8153-c802-4a28-b03f-6e734b4ae49a", 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [ 435 | "ts_ids = df.index.levels[0]\n", 436 | "new_idx = pd.MultiIndex.from_product([ts_ids, \n", 437 | " predict_data_time], \n", 438 | " names=df.index.names)\n", 439 | "\n", 440 | "df_predict = pd.DataFrame(data=df,\n", 441 | " index=new_idx, \n", 442 | " )" 443 | ] 444 | }, 445 | { 446 | "cell_type": "markdown", 447 | "id": "d676e7cc-7723-404f-a40e-2219d681d257", 448 | "metadata": {}, 449 | "source": [ 450 | "We naively project the last value carried forward for each feature for all time series into the forecast horizon. We could generate different forecasting scenarios by manipulating the future values of our variables here (e.g., `sell_price` or the `snap_` variables), for simplicity, we just carry the last value forward into the forecasting horizon (this could be problematic for the `event_` variables, we don't want to project every future day as Christmas day)." 451 | ] 452 | }, 453 | { 454 | "cell_type": "code", 455 | "execution_count": 9, 456 | "id": "43178afc-d5ca-4ce4-9785-3148abd363d0", 457 | "metadata": {}, 458 | "outputs": [], 459 | "source": [ 460 | "df_predict = df_predict.groupby(level=0).fillna(method=\"ffill\")" 461 | ] 462 | }, 463 | { 464 | "cell_type": "markdown", 465 | "id": "1fdfd88f-9db3-4ce3-8ae0-fe3518140be9", 466 | "metadata": {}, 467 | "source": [ 468 | "Remove any values of y during the forecast horizon. Just for sanity, not really required." 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": 10, 474 | "id": "c4fe44df-d6b9-44e7-b8c3-5ca458ee47b1", 475 | "metadata": {}, 476 | "outputs": [], 477 | "source": [ 478 | "time_index = df_predict.index.get_level_values(-1)\n", 479 | "time_mask = (time_index >= forecast_horizon[0]) & (time_index <= forecast_horizon[-1])\n", 480 | "df_predict.loc[time_mask, [\"y\"]] = np.NaN" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "id": "c249d427-adad-4c0b-8b1a-d89ce9430e19", 486 | "metadata": {}, 487 | "source": [ 488 | "Check that our feature engineering pipeline is functioning as expected." 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": 11, 494 | "id": "e759f009-45f7-449d-9ca8-795a5dd57c11", 495 | "metadata": {}, 496 | "outputs": [ 497 | { 498 | "data": { 499 | "text/html": [ 500 | "
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yearmonth_of_yearweek_of_yearweek_of_monthday_of_monthday_of_weekis_weekendtime_since_2012-01-01 00:00:00y_lag_1y_lag_2...sell_price_lag_7sell_price_lag_14sell_price_lag_28sell_price_mean_1_7sell_price_mean_1_14sell_price_mean_1_28snap_CAsnap_TXsnap_WIsell_price
iddate
HOBBIES_1_001_CA_1_evaluation2016-04-221.00.2727270.2884620.750.7000000.6666670.00.9812850.0046300.001543...0.2702620.2663870.2663870.2702620.2678570.2660530.00.00.00.270262
2016-04-231.00.2727270.2884620.750.7333330.8333331.00.9819090.0000000.004630...0.2702620.2702620.2663870.2702620.2681350.2661920.00.00.00.270262
2016-04-241.00.2727270.2884620.750.7666671.0000001.00.9825330.0015430.000000...0.2702620.2702620.2663870.2702620.2681350.2663300.00.00.00.270262
2016-04-251.00.2727270.3076920.750.8000000.0000000.00.9831570.0015430.001543...0.2702620.2702620.2663870.2702620.2681350.2664690.00.00.00.270262
2016-04-261.00.2727270.3076920.750.8333330.1666670.00.9837800.0000000.001543...0.2702620.2702620.2663870.2702620.2681350.2666080.00.00.00.270262
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5 rows × 30 columns

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" 693 | ], 694 | "text/plain": [ 695 | " year month_of_year week_of_year \\\n", 696 | "id date \n", 697 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 1.0 0.272727 0.288462 \n", 698 | " 2016-04-23 1.0 0.272727 0.288462 \n", 699 | " 2016-04-24 1.0 0.272727 0.288462 \n", 700 | " 2016-04-25 1.0 0.272727 0.307692 \n", 701 | " 2016-04-26 1.0 0.272727 0.307692 \n", 702 | "\n", 703 | " week_of_month day_of_month \\\n", 704 | "id date \n", 705 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.75 0.700000 \n", 706 | " 2016-04-23 0.75 0.733333 \n", 707 | " 2016-04-24 0.75 0.766667 \n", 708 | " 2016-04-25 0.75 0.800000 \n", 709 | " 2016-04-26 0.75 0.833333 \n", 710 | "\n", 711 | " day_of_week is_weekend \\\n", 712 | "id date \n", 713 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.666667 0.0 \n", 714 | " 2016-04-23 0.833333 1.0 \n", 715 | " 2016-04-24 1.000000 1.0 \n", 716 | " 2016-04-25 0.000000 0.0 \n", 717 | " 2016-04-26 0.166667 0.0 \n", 718 | "\n", 719 | " time_since_2012-01-01 00:00:00 \\\n", 720 | "id date \n", 721 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.981285 \n", 722 | " 2016-04-23 0.981909 \n", 723 | " 2016-04-24 0.982533 \n", 724 | " 2016-04-25 0.983157 \n", 725 | " 2016-04-26 0.983780 \n", 726 | "\n", 727 | " y_lag_1 y_lag_2 ... \\\n", 728 | "id date ... \n", 729 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.004630 0.001543 ... \n", 730 | " 2016-04-23 0.000000 0.004630 ... \n", 731 | " 2016-04-24 0.001543 0.000000 ... \n", 732 | " 2016-04-25 0.001543 0.001543 ... \n", 733 | " 2016-04-26 0.000000 0.001543 ... \n", 734 | "\n", 735 | " sell_price_lag_7 sell_price_lag_14 \\\n", 736 | "id date \n", 737 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.270262 0.266387 \n", 738 | " 2016-04-23 0.270262 0.270262 \n", 739 | " 2016-04-24 0.270262 0.270262 \n", 740 | " 2016-04-25 0.270262 0.270262 \n", 741 | " 2016-04-26 0.270262 0.270262 \n", 742 | "\n", 743 | " sell_price_lag_28 \\\n", 744 | "id date \n", 745 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.266387 \n", 746 | " 2016-04-23 0.266387 \n", 747 | " 2016-04-24 0.266387 \n", 748 | " 2016-04-25 0.266387 \n", 749 | " 2016-04-26 0.266387 \n", 750 | "\n", 751 | " sell_price_mean_1_7 \\\n", 752 | "id date \n", 753 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.270262 \n", 754 | " 2016-04-23 0.270262 \n", 755 | " 2016-04-24 0.270262 \n", 756 | " 2016-04-25 0.270262 \n", 757 | " 2016-04-26 0.270262 \n", 758 | "\n", 759 | " sell_price_mean_1_14 \\\n", 760 | "id date \n", 761 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.267857 \n", 762 | " 2016-04-23 0.268135 \n", 763 | " 2016-04-24 0.268135 \n", 764 | " 2016-04-25 0.268135 \n", 765 | " 2016-04-26 0.268135 \n", 766 | "\n", 767 | " sell_price_mean_1_28 snap_CA \\\n", 768 | "id date \n", 769 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.266053 0.0 \n", 770 | " 2016-04-23 0.266192 0.0 \n", 771 | " 2016-04-24 0.266330 0.0 \n", 772 | " 2016-04-25 0.266469 0.0 \n", 773 | " 2016-04-26 0.266608 0.0 \n", 774 | "\n", 775 | " snap_TX snap_WI sell_price \n", 776 | "id date \n", 777 | "HOBBIES_1_001_CA_1_evaluation 2016-04-22 0.0 0.0 0.270262 \n", 778 | " 2016-04-23 0.0 0.0 0.270262 \n", 779 | " 2016-04-24 0.0 0.0 0.270262 \n", 780 | " 2016-04-25 0.0 0.0 0.270262 \n", 781 | " 2016-04-26 0.0 0.0 0.270262 \n", 782 | "\n", 783 | "[5 rows x 30 columns]" 784 | ] 785 | }, 786 | "execution_count": 11, 787 | "metadata": {}, 788 | "output_type": "execute_result" 789 | } 790 | ], 791 | "source": [ 792 | "pipeline.transform(df_predict.head())" 793 | ] 794 | }, 795 | { 796 | "cell_type": "markdown", 797 | "id": "637cc450-18fa-4be2-a5be-182b91129741", 798 | "metadata": {}, 799 | "source": [ 800 | "# Make forecast" 801 | ] 802 | }, 803 | { 804 | "cell_type": "markdown", 805 | "id": "dc1eded8-7b96-4ca2-8752-80646eef5e10", 806 | "metadata": {}, 807 | "source": [ 808 | "Now that we have:\n", 809 | "* a trained one-step ahead forecasting model `model`;\n", 810 | "* the feature engineering pipeline to recursively computer our features `pipeline`;\n", 811 | "* the predict dataset that contains the future values of our features `df_predict`;\n", 812 | "\n", 813 | "we can build our multi-step forecast using recursive forecasting." 814 | ] 815 | }, 816 | { 817 | "cell_type": "code", 818 | "execution_count": 12, 819 | "id": "8e3c7e95-79d3-4f7c-bc9b-a24392149d2b", 820 | "metadata": {}, 821 | "outputs": [], 822 | "source": [ 823 | "# --- RECURSIVE FORECASTING LOOP --- #\n", 824 | "for forecast_time in forecast_horizon: \n", 825 | " # Compute features during the forecast horizon\n", 826 | " X_test = pipeline.transform(df_predict)\n", 827 | " time_mask = X_test.index.get_level_values(-1) == forecast_time\n", 828 | " X_test_ = X_test.loc[time_mask] \n", 829 | "\n", 830 | " # Predict one step ahead. \n", 831 | " y_pred = model.predict(X_test_)\n", 832 | " \n", 833 | " # Append forecast to the target variable columnn in our\n", 834 | " # dynamic forecast dataframe `df_predict`. This `df_predict`\n", 835 | " # is ready for the next iteration where we will re-compute\n", 836 | " # features derived from the target such as lags and windows.\n", 837 | " df_predict.loc[time_mask, [\"y\"]] = y_pred" 838 | ] 839 | }, 840 | { 841 | "cell_type": "code", 842 | "execution_count": 13, 843 | "id": "ad84f625-bbbf-4569-8815-4ecea9e9187d", 844 | "metadata": {}, 845 | "outputs": [], 846 | "source": [ 847 | "# --- GET FORECAST FROM PREDICT DATAFRAME--- # \n", 848 | "time_mask = (df_predict.index.get_level_values(-1) >= forecast_horizon[0]) & (\n", 849 | " df_predict.index.get_level_values(-1) <= forecast_horizon[-1]\n", 850 | ")\n", 851 | "\n", 852 | "df_predict[\"is_forecast\"] = time_mask\n", 853 | "y_pred = df_predict.loc[time_mask, [\"y\"]]" 854 | ] 855 | }, 856 | { 857 | "cell_type": "markdown", 858 | "id": "64111606-dd43-48b5-90be-83a547f970bb", 859 | "metadata": {}, 860 | "source": [ 861 | "Our outputs are:\n", 862 | "\n", 863 | " - `df_predict`: contains the actual values of `y` before the forecast horizon and contains the forecasted values of `y` after the forecast horizon. It also contains the original variables used to build our features.\n", 864 | "\n", 865 | " - `y_pred`: contains just our forecasted values of `y`.\n", 866 | "\n", 867 | "- `X_test`: contains the actual feature values passed to `model.predict` during the forecast horizon." 868 | ] 869 | }, 870 | { 871 | "cell_type": "markdown", 872 | "id": "ccc2ca5f-1ed2-481f-8b19-b41f032941d1", 873 | "metadata": {}, 874 | "source": [ 875 | "Let's check the predict dataframe, `df_predict`, and the dataframe with our forecasts, `y_pred`." 876 | ] 877 | }, 878 | { 879 | "cell_type": "code", 880 | "execution_count": 14, 881 | "id": "01bb63a0-d18a-441b-bd1c-824f8d260fe7", 882 | "metadata": {}, 883 | "outputs": [ 884 | { 885 | "data": { 886 | "text/html": [ 887 | "
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item_iddept_idcat_idstate_idyevent_name_1event_type_1event_name_2event_type_2snap_CAsnap_TXsnap_WIsell_pricestore_idis_forecast
iddate
HOBBIES_1_001_CA_1_evaluation2016-04-22HOBBIES_1_001HOBBIES_1HOBBIESCA0.0no_eventno_eventno_eventno_event0.00.00.08.38CA_1False
2016-04-23HOBBIES_1_001HOBBIES_1HOBBIESCA1.0no_eventno_eventno_eventno_event0.00.00.08.38CA_1False
2016-04-24HOBBIES_1_001HOBBIES_1HOBBIESCA1.0no_eventno_eventno_eventno_event0.00.00.08.38CA_1False
2016-04-25HOBBIES_1_001HOBBIES_1HOBBIESCA0.0no_eventno_eventno_eventno_event0.00.00.08.38CA_1False
2016-04-26HOBBIES_1_001HOBBIES_1HOBBIESCA0.0no_eventno_eventno_eventno_event0.00.00.08.38CA_1False
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y
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HOBBIES_1_001_CA_1_evaluation2016-05-231.045888
2016-05-240.912308
2016-05-250.939688
2016-05-261.009833
2016-05-271.104322
\n", 1145 | "
" 1146 | ], 1147 | "text/plain": [ 1148 | " y\n", 1149 | "id date \n", 1150 | "HOBBIES_1_001_CA_1_evaluation 2016-05-23 1.045888\n", 1151 | " 2016-05-24 0.912308\n", 1152 | " 2016-05-25 0.939688\n", 1153 | " 2016-05-26 1.009833\n", 1154 | " 2016-05-27 1.104322" 1155 | ] 1156 | }, 1157 | "metadata": {}, 1158 | "output_type": "display_data" 1159 | } 1160 | ], 1161 | "source": [ 1162 | "display(df_predict.head(), y_pred.head())" 1163 | ] 1164 | }, 1165 | { 1166 | "cell_type": "markdown", 1167 | "id": "238ad490-0177-4851-868e-95ebf360ba16", 1168 | "metadata": {}, 1169 | "source": [ 1170 | "Let's save the forecast and features over the forecast horizon." 1171 | ] 1172 | }, 1173 | { 1174 | "cell_type": "code", 1175 | "execution_count": 15, 1176 | "id": "5db5015b-aa7a-4f96-9e78-6430f990beb4", 1177 | "metadata": {}, 1178 | "outputs": [], 1179 | "source": [ 1180 | "f_out = forecast_dir / \"forecast.parquet\"\n", 1181 | "y_pred.to_parquet(f_out)\n", 1182 | "\n", 1183 | "f_out = forecast_dir / \"predict_dataset.parquet\"\n", 1184 | "df_predict.to_parquet(f_out)\n", 1185 | "\n", 1186 | "f_out = forecast_dir / \"predict_dataset_features.parquet\"\n", 1187 | "X_test.to_parquet(f_out)" 1188 | ] 1189 | } 1190 | ], 1191 | "metadata": { 1192 | "kernelspec": { 1193 | "display_name": "Python 3 (ipykernel)", 1194 | "language": "python", 1195 | "name": "python3" 1196 | }, 1197 | "language_info": { 1198 | "codemirror_mode": { 1199 | "name": "ipython", 1200 | "version": 3 1201 | }, 1202 | "file_extension": ".py", 1203 | "mimetype": "text/x-python", 1204 | "name": "python", 1205 | "nbconvert_exporter": "python", 1206 | "pygments_lexer": "ipython3", 1207 | "version": "3.9.10" 1208 | } 1209 | }, 1210 | "nbformat": 4, 1211 | "nbformat_minor": 5 1212 | } 1213 | -------------------------------------------------------------------------------- /notebooks/images/recursive_forecasting/Slide1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KishManani/m5-forecasting-tutorial/ae855cf5d51635f7a8740cbccdd8299e918a069f/notebooks/images/recursive_forecasting/Slide1.png -------------------------------------------------------------------------------- /notebooks/images/recursive_forecasting/Slide2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KishManani/m5-forecasting-tutorial/ae855cf5d51635f7a8740cbccdd8299e918a069f/notebooks/images/recursive_forecasting/Slide2.png -------------------------------------------------------------------------------- /notebooks/images/recursive_forecasting/Slide3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KishManani/m5-forecasting-tutorial/ae855cf5d51635f7a8740cbccdd8299e918a069f/notebooks/images/recursive_forecasting/Slide3.png -------------------------------------------------------------------------------- /notebooks/images/recursive_forecasting/Slide4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KishManani/m5-forecasting-tutorial/ae855cf5d51635f7a8740cbccdd8299e918a069f/notebooks/images/recursive_forecasting/Slide4.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.23.5 2 | pandas==1.5.2 3 | scikit-learn==1.2.0 4 | joblib==1.2.0 5 | matplotlib==3.6.3 6 | matplotlib-inline==0.1.6 7 | sktime==0.16.0 8 | jupyter_client==7.4.9 9 | jupyter_core==5.1.3 10 | pyarrow==10.0.1 11 | jupyterlab==3.6.1 12 | jupyterlab-pygments==0.2.2 13 | jupyterlab_server==2.19.0 14 | lightgbm==3.3.4 15 | --------------------------------------------------------------------------------