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AtsPy Feb/2020.ipynb
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atspy-feb-2020.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "AtsPy Feb/2020.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyMG4iiO7n+vHaJNCPaJJvi7",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/firmai/9fbcede7df4acf107f72c75fc435cd2b/atspy-feb-2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G6XiWa0wGiY1",
"colab_type": "text"
},
"source": [
"# [Automated Time Series Models in Python (AtsPy)](https://github.com/firmai/atspy)\n",
"\n",
"Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model. This is a fledgling project, all advice appreciated. \n",
"\n",
"#### Install\n",
"```\n",
"pip install atspy\n",
"```\n",
"\n",
"#### Automated Models\n",
"\n",
"1. ```ARIMA``` - Automated ARIMA Modelling\n",
"1. ```Prophet``` - Modeling Multiple Seasonality With Linear or Non-linear Growth\n",
"1. ```HWAAS``` - Exponential Smoothing With Additive Trend and Additive Seasonality\n",
"1. ```HWAMS``` - Exponential Smoothing with Additive Trend and Multiplicative Seasonality\n",
"1. ```PYAF``` - Feature Generating Model (slow and underforms)\n",
"1. ```NBEATS``` - Neural basis expansion analysis (now fixed at 20 Epochs)\n",
"1. ```Gluonts``` - RNN-based Model (now fixed at 20 Epochs)\n",
"1. ```TATS``` - Seasonal and Trend no Box Cox\n",
"1. ```TBAT``` - Trend and Box Cox\n",
"1. ```TBATS1``` - Trend, Seasonal (one), and Box Cox\n",
"1. ```TBATP1``` - TBATS1 but Seasonal Inference is Hardcoded by Periodicity\n",
"1. ```TBATS2``` - TBATS1 With Two Seasonal Periods\n",
"\n",
"#### Why AtsPy?\n",
"\n",
"1. Implements all your favourite automated time series models in a unified manner by simply running ```AutomatedModel(df)```.\n",
"1. Reduce structural model errors with 30%-50% by using LightGBM with TSFresh infused features. \n",
"1. Automatically identify the seasonalities in your data using singular spectrum analysis, periodograms, and peak analysis.\n",
"1. Identifies and makes accessible the best model for your time series using in-sample validation methods. \n",
"1. Combines the predictions of all these models in a simple (average) and complex (GBM) ensembles for improved performance.\n",
"1. Where appropriate models have been developed to use GPU resources to speed up the automation process.\n",
"1. Easily access all the models by using ```am.models_dict_in``` for in-sample and ```am.models_dict_out``` for out-of-sample prediction."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wtWJCQO-OKFy",
"colab_type": "text"
},
"source": [
"## NB Load The Package and Then \"Runtime\" \"Restart Runtime\" For Everything To Load Correctly. (Colab Issues)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LKibabzFGzhA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "c956ae7f-73d7-4b04-b072-9cc7698d10a2"
},
"source": [
"!pip install atspy"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting atspy\n",
" Downloading https://files.pythonhosted.org/packages/d6/0a/eb8b49a263de9a51fe3633794ea2fe097864426936c1ef1a3a5319c4dedf/atspy-0.2.5.tar.gz\n",
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"Collecting Cython==0.29.14\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/df/d1/4d3f8a7a920e805488a966cc6ab55c978a712240f584445d703c08b9f405/Cython-0.29.14-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
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"Collecting tbats==1.0.9\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/5e/f2/04545b598030cd72807847d9230a5db619658be3a650e112ed18acb3a122/tbats-1.0.9-py3-none-any.whl (43kB)\n",
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" Downloading https://files.pythonhosted.org/packages/5c/b1/8718eb745b993d00f176d736e3aeb6850aa97eb6e279ff13493f9e4f3b93/nbeats_pytorch-1.3.0-py3-none-any.whl\n",
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"Collecting tensorboard<1.16.0,>=1.15.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
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"Collecting tensorflow-estimator==1.15.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
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"Collecting partd>=0.3.10; extra == \"dataframe\"\n",
" Downloading https://files.pythonhosted.org/packages/44/e1/68dbe731c9c067655bff1eca5b7d40c20ca4b23fd5ec9f3d17e201a6f36b/partd-1.1.0-py3-none-any.whl\n",
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"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->tweepy==3.6.0->atspy) (3.1.0)\n",
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"Collecting locket\n",
" Downloading https://files.pythonhosted.org/packages/d0/22/3c0f97614e0be8386542facb3a7dcfc2584f7b83608c02333bced641281c/locket-0.2.0.tar.gz\n",
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"Building wheels for collected packages: atspy, fbprophet, tflearn, ujson, gast, locket\n",
" Building wheel for atspy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for atspy: filename=atspy-0.2.5-cp36-none-any.whl size=16423 sha256=283c419788bdc40f6525e72bf366d3eba3c32bbcb1bded1dc38ea39f302060b1\n",
" Stored in directory: /root/.cache/pip/wheels/64/ed/2d/e68c2d37ffc9909664c06c5e3d69bbdba2db1587ba784a534e\n",
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" Created wheel for fbprophet: filename=fbprophet-0.5-cp36-none-any.whl size=6642297 sha256=b2b7527c64552a984b2a3ee1c9d2ba0d380bd4768e409405b005fe0b750ebf02\n",
" Stored in directory: /root/.cache/pip/wheels/16/61/45/2dce71b1055b65f28a8b75702664adee6c645e3e294178a202\n",
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" Stored in directory: /root/.cache/pip/wheels/d0/f6/69/0ef3ee395aac2e5d15d89efd29a9a216f3c27767b43b72c006\n",
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" Stored in directory: /root/.cache/pip/wheels/28/77/e4/0311145b9c2e2f01470e744855131f9e34d6919687550f87d1\n",
" Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=5d2ab2f62d0e5702744debe906913472e6fe442bcb14e3d77bf0b4f8e24530d1\n",
" Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
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" Created wheel for locket: filename=locket-0.2.0-cp36-none-any.whl size=4040 sha256=325afdcfed76894ba800fdbb29120e0c9e8ff13f03b7a0d6885e0066acc02441\n",
" Stored in directory: /root/.cache/pip/wheels/26/1e/e8/4fa236ec931b1a0cdd61578e20d4934d7bf188858723b84698\n",
"Successfully built atspy fbprophet tflearn ujson gast locket\n",
"\u001b[31mERROR: umap-learn 0.4.1 has requirement numba!=0.47,>=0.46, but you'll have numba 0.47.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: tensorflow-probability 0.10.0rc0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: spacy 2.2.4 has requirement tqdm<5.0.0,>=4.38.0, but you'll have tqdm 4.28.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.0.0; python_version >= \"3.0\", but you'll have pandas 0.25.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-auth 1.7.2 has requirement cachetools<3.2,>=2.0.0, but you'll have cachetools 4.0.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: mxnet-cu100 1.4.1 has requirement numpy<1.15.0,>=1.8.2, but you'll have numpy 1.17.4 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: nbeats-pytorch 1.3.0 has requirement numpy==1.16.2, but you'll have numpy 1.17.4 which is incompatible.\u001b[0m\n",
"Installing collected packages: cachetools, Cython, zope.interface, DateTime, decorator, numpy, python-dateutil, pandas, matplotlib, lunardate, fbprophet, tqdm, ujson, pydantic, gluonts, gunicorn, Keras, graphviz, mxnet-cu100, numba, pip-tools, scikit-learn, pmdarima, seaborn, tbats, gast, tensorboard, tensorflow-estimator, tensorflow, tflearn, tsfresh, seasonal, nbeats-pytorch, atspy, locket, partd\n",
" Found existing installation: cachetools 3.1.1\n",
" Uninstalling cachetools-3.1.1:\n",
" Successfully uninstalled cachetools-3.1.1\n",
" Found existing installation: Cython 0.29.16\n",
" Uninstalling Cython-0.29.16:\n",
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" Found existing installation: decorator 4.4.2\n",
" Uninstalling decorator-4.4.2:\n",
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" Found existing installation: numpy 1.18.2\n",
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" Found existing installation: python-dateutil 2.8.1\n",
" Uninstalling python-dateutil-2.8.1:\n",
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" Found existing installation: pandas 1.0.3\n",
" Uninstalling pandas-1.0.3:\n",
" Successfully uninstalled pandas-1.0.3\n",
" Found existing installation: matplotlib 3.2.1\n",
" Uninstalling matplotlib-3.2.1:\n",
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" Found existing installation: fbprophet 0.6\n",
" Uninstalling fbprophet-0.6:\n",
" Successfully uninstalled fbprophet-0.6\n",
" Found existing installation: tqdm 4.38.0\n",
" Uninstalling tqdm-4.38.0:\n",
" Successfully uninstalled tqdm-4.38.0\n",
" Found existing installation: Keras 2.3.1\n",
" Uninstalling Keras-2.3.1:\n",
" Successfully uninstalled Keras-2.3.1\n",
" Found existing installation: graphviz 0.10.1\n",
" Uninstalling graphviz-0.10.1:\n",
" Successfully uninstalled graphviz-0.10.1\n",
" Found existing installation: numba 0.48.0\n",
" Uninstalling numba-0.48.0:\n",
" Successfully uninstalled numba-0.48.0\n",
" Found existing installation: pip-tools 4.5.1\n",
" Uninstalling pip-tools-4.5.1:\n",
" Successfully uninstalled pip-tools-4.5.1\n",
" Found existing installation: scikit-learn 0.22.2.post1\n",
" Uninstalling scikit-learn-0.22.2.post1:\n",
" Successfully uninstalled scikit-learn-0.22.2.post1\n",
" Found existing installation: seaborn 0.10.0\n",
" Uninstalling seaborn-0.10.0:\n",
" Successfully uninstalled seaborn-0.10.0\n",
" Found existing installation: gast 0.3.3\n",
" Uninstalling gast-0.3.3:\n",
" Successfully uninstalled gast-0.3.3\n",
" Found existing installation: tensorboard 2.2.1\n",
" Uninstalling tensorboard-2.2.1:\n",
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" Found existing installation: tensorflow-estimator 2.2.0\n",
" Uninstalling tensorflow-estimator-2.2.0:\n",
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" Found existing installation: tensorflow 2.2.0rc3\n",
" Uninstalling tensorflow-2.2.0rc3:\n",
" Successfully uninstalled tensorflow-2.2.0rc3\n",
"Successfully installed Cython-0.29.14 DateTime-4.3 Keras-2.2.5 atspy-0.2.5 cachetools-4.0.0 decorator-4.4.1 fbprophet-0.5 gast-0.2.2 gluonts-0.4.2 graphviz-0.8.4 gunicorn-20.0.4 locket-0.2.0 lunardate-0.2.0 matplotlib-3.1.2 mxnet-cu100-1.4.1 nbeats-pytorch-1.3.0 numba-0.47.0 numpy-1.17.4 pandas-0.25.3 partd-1.1.0 pip-tools-4.2.0 pmdarima-1.5.2 pydantic-1.5 python-dateutil-2.8.0 scikit-learn-0.22.1 seaborn-0.9.1 seasonal-0.3.1 tbats-1.0.9 tensorboard-1.15.0 tensorflow-1.15.0 tensorflow-estimator-1.15.1 tflearn-0.3.2 tqdm-4.28.1 tsfresh-0.14.1 ujson-1.35 zope.interface-5.1.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"cachetools",
"dateutil",
"decorator",
"matplotlib",
"mpl_toolkits",
"numpy",
"pandas",
"tqdm"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fI_BWrPVGsBA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 237
},
"outputId": "9b575a2f-0602-42ad-aeb0-40c779f71e8a"
},
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/firmai/random-assets-two/master/ts/monthly-beer-australia.csv\")\n",
"df.Month = pd.to_datetime(df.Month)\n",
"df = df.set_index(\"Month\")\n",
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Megaliters</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Month</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1956-01-01</th>\n",
" <td>93.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956-02-01</th>\n",
" <td>96.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956-03-01</th>\n",
" <td>95.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956-04-01</th>\n",
" <td>77.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1956-05-01</th>\n",
" <td>70.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Megaliters\n",
"Month \n",
"1956-01-01 93.2\n",
"1956-02-01 96.0\n",
"1956-03-01 95.2\n",
"1956-04-01 77.1\n",
"1956-05-01 70.9"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9K_YU202HCjR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "1eb5b089-2002-455e-daf5-313d176d5597"
},
"source": [
"from atspy import AutomatedModel"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:root:Using GPU\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KWfyc4ZFHMXa",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 523
},
"outputId": "fa330b5c-aa95-43b5-c864-68e11f65d0fe"
},
"source": [
"model_list=[\"HWAMS\",\"HWAAS\",\"TBAT\"]\n",
"\n",
"am = AutomatedModel(df = df , model_list=model_list, season=\"infer_from_data\",forecast_len=60 )\n",
"forecast_in, performance = am.forecast_insample()\n",
"forecast_out = am.forecast_outsample()\n",
"all_ensemble_in, all_ensemble_out, all_performance = am.ensemble(forecast_in, forecast_out)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"The data has been successfully parsed by infering a frequency, and establishing a 'Date' index and 'Target' column.\n",
"357\n",
"An insample split of training size 357 and testing size 119 has been constructed\n",
"Model HWAMS is being trained for in sample prediction\n",
"Model HWAAS is being trained for in sample prediction\n",
"Model TBAT is being trained for in sample prediction\n",
"Model HWAMS is being used to forcast in sample\n",
"Model HWAAS is being used to forcast in sample\n",
"Model TBAT is being used to forcast in sample\n",
"Successfully finished in sample forecast\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"The data has been successfully parsed by infering a frequency, and establishing a 'Date' index and 'Target' column.\n",
"Model HWAMS is being trained for out of sample prediction\n",
"Model HWAAS is being trained for out of sample prediction\n",
"Model TBAT is being trained for out of sample prediction\n",
"Model HWAMS is being used to forcast out of sample\n",
"Model HWAAS is being used to forcast out of sample\n",
"Model TBAT is being used to forcast out of sample\n",
"Successfully finished out of sample forecast\n",
"Building LightGBM Ensemble from TS data (ensemble_lgb)\n",
"Building LightGBM Ensemble from PCA reduced TSFresh Features (ensemble_ts). This can take a long time.\n",
"LightGBM ensemble have been successfully built\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"305 variables are found to be almost constant\n",
"LightGBM ensemble have been successfully built\n",
"Building Standard First Level Ensemble\n",
"Building Final Multi-level Ensemble\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BvM1I7ABSADt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "3ba54881-9683-428e-e8fa-8639b3f175f0"
},
"source": [
"forecast_in.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Target</th>\n",
" <th>HWAMS</th>\n",
" <th>HWAAS</th>\n",
" <th>TBAT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1985-10-01</th>\n",
" <td>181.6</td>\n",
" <td>161.962148</td>\n",
" <td>162.391653</td>\n",
" <td>148.410071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985-11-01</th>\n",
" <td>182.0</td>\n",
" <td>174.688055</td>\n",
" <td>173.191756</td>\n",
" <td>147.999237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985-12-01</th>\n",
" <td>190.0</td>\n",
" <td>189.728744</td>\n",
" <td>187.649575</td>\n",
" <td>147.589541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986-01-01</th>\n",
" <td>161.2</td>\n",
" <td>155.077205</td>\n",
" <td>154.817215</td>\n",
" <td>147.180980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986-02-01</th>\n",
" <td>155.5</td>\n",
" <td>148.054292</td>\n",
" <td>147.477692</td>\n",
" <td>146.773549</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Target HWAMS HWAAS TBAT\n",
"Date \n",
"1985-10-01 181.6 161.962148 162.391653 148.410071\n",
"1985-11-01 182.0 174.688055 173.191756 147.999237\n",
"1985-12-01 190.0 189.728744 187.649575 147.589541\n",
"1986-01-01 161.2 155.077205 154.817215 147.180980\n",
"1986-02-01 155.5 148.054292 147.477692 146.773549"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CdKZNjauyJ7M",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "7b5c2cda-7327-4aaa-ca9c-30252cc4244c"
},
"source": [
"forecast_out.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1995-09-01</th>\n",
" <td>137.518755</td>\n",
" <td>137.133938</td>\n",
" <td>142.906275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995-10-01</th>\n",
" <td>164.136220</td>\n",
" <td>165.079612</td>\n",
" <td>142.865575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995-11-01</th>\n",
" <td>178.671684</td>\n",
" <td>180.009560</td>\n",
" <td>142.827110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995-12-01</th>\n",
" <td>184.175954</td>\n",
" <td>185.715043</td>\n",
" <td>142.790757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-01-01</th>\n",
" <td>147.166448</td>\n",
" <td>147.440026</td>\n",
" <td>142.756399</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HWAMS HWAAS TBAT\n",
"Date \n",
"1995-09-01 137.518755 137.133938 142.906275\n",
"1995-10-01 164.136220 165.079612 142.865575\n",
"1995-11-01 178.671684 180.009560 142.827110\n",
"1995-12-01 184.175954 185.715043 142.790757\n",
"1996-01-01 147.166448 147.440026 142.756399"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L6-vdrTqR5Pd",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"outputId": "a7fa0f19-03b2-444c-8c9d-43b7cbc8767e"
},
"source": [
"performance"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>Target</th>\n",
" <th>HWAMS</th>\n",
" <th>HWAAS</th>\n",
" <th>TBAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>rmse</th>\n",
" <td>0.000000</td>\n",
" <td>17.599400</td>\n",
" <td>18.993827</td>\n",
" <td>36.538009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mse</th>\n",
" <td>0.000000</td>\n",
" <td>309.738878</td>\n",
" <td>360.765452</td>\n",
" <td>1335.026136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>155.293277</td>\n",
" <td>142.399639</td>\n",
" <td>140.577496</td>\n",
" <td>126.590412</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Target HWAMS HWAAS TBAT\n",
"rmse 0.000000 17.599400 18.993827 36.538009\n",
"mse 0.000000 309.738878 360.765452 1335.026136\n",
"mean 155.293277 142.399639 140.577496 126.590412"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kEiMYg9pM3Dg",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 414
},
"outputId": "c3110f16-c3c4-4f7e-cf41-7012344c6874"
},
"source": [
"all_performance"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rmse</th>\n",
" <th>mse</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ensemble_lgb__X__HWAMS</th>\n",
" <td>9.697588</td>\n",
" <td>94.043213</td>\n",
" <td>146.719412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS</th>\n",
" <td>9.875212</td>\n",
" <td>97.519817</td>\n",
" <td>145.250837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS</th>\n",
" <td>11.127326</td>\n",
" <td>123.817378</td>\n",
" <td>142.994374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ensemble_lgb</th>\n",
" <td>12.748526</td>\n",
" <td>162.524907</td>\n",
" <td>156.487208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS__X__HWAMS_HWAAS_TBAT__X__TBAT</th>\n",
" <td>14.589155</td>\n",
" <td>212.843442</td>\n",
" <td>138.615567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWAMS</th>\n",
" <td>15.567905</td>\n",
" <td>242.359663</td>\n",
" <td>136.951615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWAMS_HWAAS</th>\n",
" <td>16.651370</td>\n",
" <td>277.268110</td>\n",
" <td>135.544299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ensemble_ts</th>\n",
" <td>17.255107</td>\n",
" <td>297.738716</td>\n",
" <td>163.134079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWAAS</th>\n",
" <td>17.804066</td>\n",
" <td>316.984751</td>\n",
" <td>134.136983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HWAMS_HWAAS_TBAT</th>\n",
" <td>23.358758</td>\n",
" <td>545.631579</td>\n",
" <td>128.785846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TBAT</th>\n",
" <td>39.003864</td>\n",
" <td>1521.301380</td>\n",
" <td>115.268940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rmse ... mean\n",
"ensemble_lgb__X__HWAMS 9.697588 ... 146.719412\n",
"ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ense... 9.875212 ... 145.250837\n",
"ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS 11.127326 ... 142.994374\n",
"ensemble_lgb 12.748526 ... 156.487208\n",
"ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ense... 14.589155 ... 138.615567\n",
"HWAMS 15.567905 ... 136.951615\n",
"HWAMS_HWAAS 16.651370 ... 135.544299\n",
"ensemble_ts 17.255107 ... 163.134079\n",
"HWAAS 17.804066 ... 134.136983\n",
"HWAMS_HWAAS_TBAT 23.358758 ... 128.785846\n",
"TBAT 39.003864 ... 115.268940\n",
"\n",
"[11 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qV6SQPQASIRQ",
"colab_type": "code",
"colab": {
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"height": 296
},
"outputId": "f88ddb37-5402-40d2-c550-d1228257aa6f"
},
"source": [
"all_ensemble_in[[\"Target\",\"ensemble_lgb__X__HWAMS\",\"HWAMS\",\"HWAAS\"]].plot()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7faf7f8f4710>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
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7HPvyDA4GGRkZNDc3s2nTJmbPng3AtGnT+Mc//kFycrKvwCU7O5u9e/f6DEKT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\nQSr/feLa9k1JdJCGqfEh7aT/LxyKs8Bl78RRa7E5Ka5v6dQ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od6hoLiXOWdFtmfrSga0E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\nKIQQQgghikUajkIIIYQQolj+H95XGKlxBNErAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 792x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3esRD1G_SZQC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"outputId": "5cb80e6f-4de2-40bf-c177-167fe22830b7"
},
"source": [
"all_ensemble_out[[\"ensemble_lgb__X__HWAMS\",\"HWAMS\",\"HWAAS\"]].plot()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7faf7f8a5588>"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"image/png": 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Hf38L+C9FZzbQJoQ4pqWrVc1VAFRseRumXdX3jcZgjD5QpBGRfERyn6b6/VAy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0Z0oFXo2WUYE+LHdd1acV0352T+xeVVbMrKZ0NWB7c283nbH0Z6r5tFTIgfUeFDgJ2s2bHAo4nj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\n",
"text/plain": [
"<Figure size 792x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7mkc1snQaJf6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"outputId": "24b7795e-08f9-4490-8a44-8507815bc589"
},
"source": [
"am.models_dict_in"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'HWAAS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7faf9002de80>,\n",
" 'HWAMS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7faf9002d9e8>,\n",
" 'TBAT': <tbats.tbats.Model.Model at 0x7faf900567b8>}"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "csb7BOy0b_Ff",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"outputId": "0de6541b-79e7-4313-f425-b7ed4ca4e1b7"
},
"source": [
"am.models_dict_out"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'HWAAS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7faf90056278>,\n",
" 'HWAMS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7faf900566d8>,\n",
" 'TBAT': <tbats.tbats.Model.Model at 0x7faf9002d400>}"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PDzgNt9lNduV",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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