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"<a href=\"https://colab.research.google.com/gist/ngupta23/038b4c363ee846e9cc6ec4229aafaac5/pycaret_ts_naas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"## Step 0: Install library (one time)"
]
},
{
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"id": "0a2Ev2hF4V8u",
"outputId": "c6b2831b-1622-4d90-fa0b-22641c0b6943"
},
"source": [
"!pip install pycaret-ts-alpha -U"
],
"execution_count": null,
"outputs": [
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"text": [
"Collecting pycaret-ts-alpha\n",
" Downloading pycaret_ts_alpha-3.0.0.dev1634867798-py3-none-any.whl (475 kB)\n",
"\u001b[K |████████████████████████████████| 475 kB 5.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: cufflinks>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.17.3)\n",
"Collecting plotly>=5.0.0\n",
" Downloading plotly-5.3.1-py2.py3-none-any.whl (23.9 MB)\n",
"\u001b[K |████████████████████████████████| 23.9 MB 13 kB/s \n",
"\u001b[?25hRequirement already satisfied: spacy<2.4.0 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (2.2.4)\n",
"Requirement already satisfied: IPython in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (5.5.0)\n",
"Collecting scikit-learn~=0.24.2\n",
" Downloading scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB)\n",
"\u001b[K |████████████████████████████████| 22.3 MB 1.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.51.2)\n",
"Collecting yellowbrick>=1.0.1\n",
" Downloading yellowbrick-1.3.post1-py3-none-any.whl (271 kB)\n",
"\u001b[K |████████████████████████████████| 271 kB 59.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.15.3)\n",
"Collecting lightgbm>=2.3.1\n",
" Downloading lightgbm-3.3.0-py3-none-manylinux1_x86_64.whl (2.0 MB)\n",
"\u001b[K |████████████████████████████████| 2.0 MB 53.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy==1.19.5 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (1.19.5)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (3.2.2)\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.11.2)\n",
"Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (1.5.0)\n",
"Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (7.6.5)\n",
"Requirement already satisfied: scipy<=1.5.4 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (1.4.1)\n",
"Collecting mlflow\n",
" Downloading mlflow-1.20.2-py3-none-any.whl (14.6 MB)\n",
"\u001b[K |████████████████████████████████| 14.6 MB 124 kB/s \n",
"\u001b[?25hRequirement already satisfied: gensim<4.0.0 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (3.6.0)\n",
"Collecting tbats>=1.1.0\n",
" Downloading tbats-1.1.0-py3-none-any.whl (43 kB)\n",
"\u001b[K |████████████████████████████████| 43 kB 2.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (1.0.1)\n",
"Collecting scikit-plot\n",
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"Installing collected packages: threadpoolctl, tangled-up-in-unicode, smmap, scipy, multimethod, llvmlite, websocket-client, visions, tenacity, statsmodels, scikit-learn, requests, python-editor, numba, Mako, imagehash, gitdb, querystring-parser, pyyaml, pynndescent, pydantic, prometheus-flask-exporter, pmdarima, plotly, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, yellowbrick, umap-learn, tbats, sktime, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret-ts-alpha\n",
" Attempting uninstall: scipy\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
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"Successfully installed Boruta-0.3 Mako-1.1.5 alembic-1.4.1 databricks-cli-0.16.2 docker-5.0.3 funcy-1.16 gitdb-4.0.9 gitpython-3.1.24 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 kmodes-0.11.1 lightgbm-3.3.0 llvmlite-0.37.0 mlflow-1.20.2 mlxtend-0.19.0 multimethod-1.6 numba-0.54.1 pandas-profiling-3.1.0 phik-0.12.0 plotly-5.3.1 pmdarima-1.8.3 prometheus-flask-exporter-0.18.4 pyLDAvis-3.2.2 pycaret-ts-alpha-3.0.0.dev1634867798 pydantic-1.8.2 pynndescent-0.5.5 pyod-0.9.4 python-editor-1.0.4 pyyaml-5.4.1 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.24.2 scikit-plot-0.3.7 scipy-1.5.4 sktime-0.8.0 smmap-5.0.0 statsmodels-0.12.2 tangled-up-in-unicode-0.1.0 tbats-1.1.0 tenacity-8.0.1 threadpoolctl-3.0.0 umap-learn-0.5.1 visions-0.7.4 websocket-client-1.2.1 yellowbrick-1.3.post1\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fvsqXdRR7s1V"
},
"source": [
"## Step 1: Load data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140
},
"id": "KoE_lgXs4ly4",
"outputId": "76e3dda7-5ecb-4d65-88ca-408a110178c3"
},
"source": [
"from pycaret.datasets import get_data\n",
"y = get_data(\"airline\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Period\n",
"1949-01 112.0\n",
"1949-02 118.0\n",
"1949-03 132.0\n",
"1949-04 129.0\n",
"1949-05 121.0\n",
"Freq: M, Name: Number of airline passengers, dtype: float64"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e_CBvHqS7xE1"
},
"source": [
"## Step 2: Setup experiment"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 682,
"referenced_widgets": [
"c05e38458d1f4f2baa6051214b575303",
"53aee6449f464210a9fe523f75a819e2",
"a9428e6de6a84f8a97b9389c0c1d8ffa"
]
},
"id": "CX7l9QjJ4pR9",
"outputId": "9f708797-4a1d-40fc-fdd7-b31f9629e74e"
},
"source": [
"from pycaret.internal.pycaret_experiment.time_series_experiment import TimeSeriesExperiment\n",
"exp = TimeSeriesExperiment()\n",
"\n",
"# NOTE: If your data does not have a datetime or period index,\n",
"# then please specify the seasonal_period parameters in setup.\n",
"exp.setup(data=y, fh=12, session_id=42)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>Description</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>session_id</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Original Data</td>\n",
" <td>(144, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Missing Values</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Transformed Train Set</td>\n",
" <td>(132,)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Transformed Test Set</td>\n",
" <td>(12,)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Fold Generator</td>\n",
" <td>ExpandingWindowSplitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Fold Number</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Enforce Prediction Interval</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Seasonal Period Tested</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Seasonality Detected</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Target Strictly Positive</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Target White Noise</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Recommended d</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Recommended Seasonal D</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>CPU Jobs</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Use GPU</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Log Experiment</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Experiment Name</td>\n",
" <td>ts-default-name</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>USI</td>\n",
" <td>ab91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Imputation Type</td>\n",
" <td>simple</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Description Value\n",
"0 session_id 42\n",
"1 Original Data (144, 1)\n",
"2 Missing Values False\n",
"3 Transformed Train Set (132,)\n",
"4 Transformed Test Set (12,)\n",
"5 Fold Generator ExpandingWindowSplitter\n",
"6 Fold Number 3\n",
"7 Enforce Prediction Interval False\n",
"8 Seasonal Period Tested 12\n",
"9 Seasonality Detected True\n",
"10 Target Strictly Positive True\n",
"11 Target White Noise No\n",
"12 Recommended d 1\n",
"13 Recommended Seasonal D 1\n",
"14 CPU Jobs -1\n",
"15 Use GPU False\n",
"16 Log Experiment False\n",
"17 Experiment Name ts-default-name\n",
"18 USI ab91\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f4669e16590>"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e0G28EkL7MMv"
},
"source": [
"## Step 3: Train Forecasters"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 819,
"referenced_widgets": [
"632eee0d9d4d4fa580fa02780859ee36",
"46e8878890a645dc807cab862c219487",
"ccabc07e1463452ebf8b61b895f3fa70"
]
},
"id": "3zKOxFfh4r_y",
"outputId": "b663b831-f4e0-41d3-f6aa-f122590e470e"
},
"source": [
"#### Step 3A: Compare multiple baseline models & return top 3 ----\n",
"baseline_models = exp.compare_models(n_select=3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>Model</th>\n",
" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
" <th>R2</th>\n",
" <th>TT (Sec)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>exp_smooth</th>\n",
" <td>Exponential Smoothing</td>\n",
" <td>16.7773</td>\n",
" <td>19.7959</td>\n",
" <td>0.0422</td>\n",
" <td>0.0427</td>\n",
" <td>0.8954</td>\n",
" <td>0.1467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ets</th>\n",
" <td>ETS</td>\n",
" <td>17.4191</td>\n",
" <td>20.5125</td>\n",
" <td>0.044</td>\n",
" <td>0.0445</td>\n",
" <td>0.8882</td>\n",
" <td>0.2167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>arima</th>\n",
" <td>ARIMA</td>\n",
" <td>20.0069</td>\n",
" <td>22.2199</td>\n",
" <td>0.0501</td>\n",
" <td>0.0507</td>\n",
" <td>0.8677</td>\n",
" <td>0.0767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>auto_arima</th>\n",
" <td>Auto ARIMA</td>\n",
" <td>21.0297</td>\n",
" <td>23.4661</td>\n",
" <td>0.0525</td>\n",
" <td>0.0531</td>\n",
" <td>0.8509</td>\n",
" <td>4.4700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>et_cds_dt</th>\n",
" <td>Extra Trees w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>24.4233</td>\n",
" <td>31.4395</td>\n",
" <td>0.0584</td>\n",
" <td>0.0601</td>\n",
" <td>0.7169</td>\n",
" <td>0.9767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>knn_cds_dt</th>\n",
" <td>K Neighbors w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>25.8293</td>\n",
" <td>34.9633</td>\n",
" <td>0.0617</td>\n",
" <td>0.0641</td>\n",
" <td>0.626</td>\n",
" <td>0.8367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rf_cds_dt</th>\n",
" <td>Random Forest w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>26.8586</td>\n",
" <td>38.0499</td>\n",
" <td>0.0629</td>\n",
" <td>0.0654</td>\n",
" <td>0.6023</td>\n",
" <td>1.0133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ada_cds_dt</th>\n",
" <td>AdaBoost w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>27.82</td>\n",
" <td>37.791</td>\n",
" <td>0.0661</td>\n",
" <td>0.0686</td>\n",
" <td>0.6015</td>\n",
" <td>0.1233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta</th>\n",
" <td>Theta Forecaster</td>\n",
" <td>28.3192</td>\n",
" <td>33.8639</td>\n",
" <td>0.067</td>\n",
" <td>0.07</td>\n",
" <td>0.671</td>\n",
" <td>0.0200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gbr_cds_dt</th>\n",
" <td>Gradient Boosting w/ Cond. Deseasonalize &amp; Det...</td>\n",
" <td>29.1314</td>\n",
" <td>38.308</td>\n",
" <td>0.0685</td>\n",
" <td>0.0715</td>\n",
" <td>0.5855</td>\n",
" <td>0.0633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lightgbm_cds_dt</th>\n",
" <td>Light Gradient Boosting w/ Cond. Deseasonalize...</td>\n",
" <td>28.7738</td>\n",
" <td>36.2392</td>\n",
" <td>0.0698</td>\n",
" <td>0.0722</td>\n",
" <td>0.6255</td>\n",
" <td>0.0333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>br_cds_dt</th>\n",
" <td>Bayesian Ridge w/ Cond. Deseasonalize &amp; Detren...</td>\n",
" <td>32.0341</td>\n",
" <td>39.2191</td>\n",
" <td>0.0799</td>\n",
" <td>0.0818</td>\n",
" <td>0.5658</td>\n",
" <td>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lasso_cds_dt</th>\n",
" <td>Lasso w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>32.8026</td>\n",
" <td>39.2084</td>\n",
" <td>0.0823</td>\n",
" <td>0.0841</td>\n",
" <td>0.5678</td>\n",
" <td>0.0300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>en_cds_dt</th>\n",
" <td>Elastic Net w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>32.8556</td>\n",
" <td>39.2557</td>\n",
" <td>0.0825</td>\n",
" <td>0.0843</td>\n",
" <td>0.5669</td>\n",
" <td>0.0233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lr_cds_dt</th>\n",
" <td>Linear w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>32.9708</td>\n",
" <td>39.3456</td>\n",
" <td>0.0828</td>\n",
" <td>0.0846</td>\n",
" <td>0.5652</td>\n",
" <td>0.0233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ridge_cds_dt</th>\n",
" <td>Ridge w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>32.9702</td>\n",
" <td>39.3452</td>\n",
" <td>0.0828</td>\n",
" <td>0.0846</td>\n",
" <td>0.5652</td>\n",
" <td>0.0233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dt_cds_dt</th>\n",
" <td>Decision Tree w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>35.1446</td>\n",
" <td>45.8861</td>\n",
" <td>0.0826</td>\n",
" <td>0.0869</td>\n",
" <td>0.4284</td>\n",
" <td>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snaive</th>\n",
" <td>Seasonal Naive Forecaster</td>\n",
" <td>33.3611</td>\n",
" <td>35.9139</td>\n",
" <td>0.0832</td>\n",
" <td>0.0879</td>\n",
" <td>0.6072</td>\n",
" <td>0.0167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>huber_cds_dt</th>\n",
" <td>Huber w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>35.4709</td>\n",
" <td>41.1489</td>\n",
" <td>0.091</td>\n",
" <td>0.0936</td>\n",
" <td>0.5226</td>\n",
" <td>0.0433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lar_cds_dt</th>\n",
" <td>Least Angular Regressor w/ Cond. Deseasonalize...</td>\n",
" <td>36.5285</td>\n",
" <td>42.4001</td>\n",
" <td>0.0936</td>\n",
" <td>0.0945</td>\n",
" <td>0.5058</td>\n",
" <td>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>llar_cds_dt</th>\n",
" <td>Lasso Least Angular Regressor w/ Cond. Deseaso...</td>\n",
" <td>46.7239</td>\n",
" <td>63.1706</td>\n",
" <td>0.1109</td>\n",
" <td>0.1165</td>\n",
" <td>-0.0733</td>\n",
" <td>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>omp_cds_dt</th>\n",
" <td>Orthogonal Matching Pursuit w/ Cond. Deseasona...</td>\n",
" <td>47.2799</td>\n",
" <td>64.5891</td>\n",
" <td>0.111</td>\n",
" <td>0.1177</td>\n",
" <td>-0.1201</td>\n",
" <td>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>polytrend</th>\n",
" <td>Polynomial Trend Forecaster</td>\n",
" <td>48.6301</td>\n",
" <td>63.4299</td>\n",
" <td>0.117</td>\n",
" <td>0.1216</td>\n",
" <td>-0.0784</td>\n",
" <td>0.0133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>naive</th>\n",
" <td>Naive Forecaster</td>\n",
" <td>69.0278</td>\n",
" <td>91.0322</td>\n",
" <td>0.1569</td>\n",
" <td>0.1792</td>\n",
" <td>-1.2216</td>\n",
" <td>1.4567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>par_cds_dt</th>\n",
" <td>Passive Aggressive w/ Cond. Deseasonalize &amp; De...</td>\n",
" <td>78.0396</td>\n",
" <td>95.4251</td>\n",
" <td>0.2137</td>\n",
" <td>0.2531</td>\n",
" <td>-3.0784</td>\n",
" <td>0.0233</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE \\\n",
"exp_smooth Exponential Smoothing 16.7773 \n",
"ets ETS 17.4191 \n",
"arima ARIMA 20.0069 \n",
"auto_arima Auto ARIMA 21.0297 \n",
"et_cds_dt Extra Trees w/ Cond. Deseasonalize & Detrending 24.4233 \n",
"knn_cds_dt K Neighbors w/ Cond. Deseasonalize & Detrending 25.8293 \n",
"rf_cds_dt Random Forest w/ Cond. Deseasonalize & Detrending 26.8586 \n",
"ada_cds_dt AdaBoost w/ Cond. Deseasonalize & Detrending 27.82 \n",
"theta Theta Forecaster 28.3192 \n",
"gbr_cds_dt Gradient Boosting w/ Cond. Deseasonalize & Det... 29.1314 \n",
"lightgbm_cds_dt Light Gradient Boosting w/ Cond. Deseasonalize... 28.7738 \n",
"br_cds_dt Bayesian Ridge w/ Cond. Deseasonalize & Detren... 32.0341 \n",
"lasso_cds_dt Lasso w/ Cond. Deseasonalize & Detrending 32.8026 \n",
"en_cds_dt Elastic Net w/ Cond. Deseasonalize & Detrending 32.8556 \n",
"lr_cds_dt Linear w/ Cond. Deseasonalize & Detrending 32.9708 \n",
"ridge_cds_dt Ridge w/ Cond. Deseasonalize & Detrending 32.9702 \n",
"dt_cds_dt Decision Tree w/ Cond. Deseasonalize & Detrending 35.1446 \n",
"snaive Seasonal Naive Forecaster 33.3611 \n",
"huber_cds_dt Huber w/ Cond. Deseasonalize & Detrending 35.4709 \n",
"lar_cds_dt Least Angular Regressor w/ Cond. Deseasonalize... 36.5285 \n",
"llar_cds_dt Lasso Least Angular Regressor w/ Cond. Deseaso... 46.7239 \n",
"omp_cds_dt Orthogonal Matching Pursuit w/ Cond. Deseasona... 47.2799 \n",
"polytrend Polynomial Trend Forecaster 48.6301 \n",
"naive Naive Forecaster 69.0278 \n",
"par_cds_dt Passive Aggressive w/ Cond. Deseasonalize & De... 78.0396 \n",
"\n",
" RMSE MAPE SMAPE R2 TT (Sec) \n",
"exp_smooth 19.7959 0.0422 0.0427 0.8954 0.1467 \n",
"ets 20.5125 0.044 0.0445 0.8882 0.2167 \n",
"arima 22.2199 0.0501 0.0507 0.8677 0.0767 \n",
"auto_arima 23.4661 0.0525 0.0531 0.8509 4.4700 \n",
"et_cds_dt 31.4395 0.0584 0.0601 0.7169 0.9767 \n",
"knn_cds_dt 34.9633 0.0617 0.0641 0.626 0.8367 \n",
"rf_cds_dt 38.0499 0.0629 0.0654 0.6023 1.0133 \n",
"ada_cds_dt 37.791 0.0661 0.0686 0.6015 0.1233 \n",
"theta 33.8639 0.067 0.07 0.671 0.0200 \n",
"gbr_cds_dt 38.308 0.0685 0.0715 0.5855 0.0633 \n",
"lightgbm_cds_dt 36.2392 0.0698 0.0722 0.6255 0.0333 \n",
"br_cds_dt 39.2191 0.0799 0.0818 0.5658 0.0267 \n",
"lasso_cds_dt 39.2084 0.0823 0.0841 0.5678 0.0300 \n",
"en_cds_dt 39.2557 0.0825 0.0843 0.5669 0.0233 \n",
"lr_cds_dt 39.3456 0.0828 0.0846 0.5652 0.0233 \n",
"ridge_cds_dt 39.3452 0.0828 0.0846 0.5652 0.0233 \n",
"dt_cds_dt 45.8861 0.0826 0.0869 0.4284 0.0267 \n",
"snaive 35.9139 0.0832 0.0879 0.6072 0.0167 \n",
"huber_cds_dt 41.1489 0.091 0.0936 0.5226 0.0433 \n",
"lar_cds_dt 42.4001 0.0936 0.0945 0.5058 0.0267 \n",
"llar_cds_dt 63.1706 0.1109 0.1165 -0.0733 0.0267 \n",
"omp_cds_dt 64.5891 0.111 0.1177 -0.1201 0.0267 \n",
"polytrend 63.4299 0.117 0.1216 -0.0784 0.0133 \n",
"naive 91.0322 0.1569 0.1792 -1.2216 1.4567 \n",
"par_cds_dt 95.4251 0.2137 0.2531 -3.0784 0.0233 "
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203,
"referenced_widgets": [
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"122ff6d1166c4689ad1b6c5b541e3f3e",
"bbf32dfcaed94d50b3f098879bd47be3",
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"d12ad8882d7a4d57b72f10bbd6bbda86",
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"id": "3Jt0xcXy5l1h",
"outputId": "eab6bffa-7c0a-49bc-cd1f-baaf0340dd41"
},
"source": [
"#### Step 3B: Perform hyperparameter tuning on the best baseline models ----\n",
"tuned_baseline_models = [exp.tune_model(model) for model in baseline_models]"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"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>cutoff</th>\n",
" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
" <th>R2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>13.2626</td>\n",
" <td>16.6689</td>\n",
" <td>0.0331</td>\n",
" <td>0.0339</td>\n",
" <td>0.9096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>19.1686</td>\n",
" <td>21.3384</td>\n",
" <td>0.0530</td>\n",
" <td>0.0513</td>\n",
" <td>0.8807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>21.1925</td>\n",
" <td>23.4747</td>\n",
" <td>0.0491</td>\n",
" <td>0.0506</td>\n",
" <td>0.8767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>17.8746</td>\n",
" <td>20.4940</td>\n",
" <td>0.0451</td>\n",
" <td>0.0453</td>\n",
" <td>0.8890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>3.3642</td>\n",
" <td>2.8419</td>\n",
" <td>0.0086</td>\n",
" <td>0.0080</td>\n",
" <td>0.0146</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 13.2626 16.6689 0.0331 0.0339 0.9096\n",
"1 1957-12 19.1686 21.3384 0.0530 0.0513 0.8807\n",
"2 1958-12 21.1925 23.4747 0.0491 0.0506 0.8767\n",
"Mean NaN 17.8746 20.4940 0.0451 0.0453 0.8890\n",
"SD NaN 3.3642 2.8419 0.0086 0.0080 0.0146"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203,
"referenced_widgets": [
"f9bcf8729406451b97de9ff69a47de7f",
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},
"id": "Nb7vzjYB53Rl",
"outputId": "56e5b586-829e-404c-fe9a-bdc4cd81dbeb"
},
"source": [
"#### Step 3C: Blend the tuned models into 1 composite estimator ----\n",
"# If performance of blender is not better than the individual \n",
"# models then choose the best individual model\n",
"blender = exp.blend_models(tuned_baseline_models, method='median', choose_better=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cutoff</th>\n",
" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
" <th>R2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>11.0061</td>\n",
" <td>14.5135</td>\n",
" <td>0.0278</td>\n",
" <td>0.0281</td>\n",
" <td>0.9314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>26.4530</td>\n",
" <td>29.9504</td>\n",
" <td>0.0742</td>\n",
" <td>0.0708</td>\n",
" <td>0.7650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>13.7404</td>\n",
" <td>15.9712</td>\n",
" <td>0.0314</td>\n",
" <td>0.0320</td>\n",
" <td>0.9429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>17.0665</td>\n",
" <td>20.1450</td>\n",
" <td>0.0445</td>\n",
" <td>0.0436</td>\n",
" <td>0.8798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>6.7305</td>\n",
" <td>6.9589</td>\n",
" <td>0.0211</td>\n",
" <td>0.0193</td>\n",
" <td>0.0813</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 11.0061 14.5135 0.0278 0.0281 0.9314\n",
"1 1957-12 26.4530 29.9504 0.0742 0.0708 0.7650\n",
"2 1958-12 13.7404 15.9712 0.0314 0.0320 0.9429\n",
"Mean NaN 17.0665 20.1450 0.0445 0.0436 0.8798\n",
"SD NaN 6.7305 6.9589 0.0211 0.0193 0.0813"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "ySLO8Om26X8L",
"outputId": "acc70bb4-91c8-4da2-ca03-c556d4ae1f7f"
},
"source": [
"#### Step 3D: Check predictions ----\n",
"exp.plot_model(blender)"
],
"execution_count": null,
"outputs": [
{
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"metadata": {}
}
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},
{
"cell_type": "markdown",
"metadata": {
"id": "7j8KaRNu7Zen"
},
"source": [
"## Step 5: Future Predictions"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JHbTKotJ6zhq",
"outputId": "6f84d6d0-5ec5-41c7-ed0c-613f24d1d2df"
},
"source": [
"#### Option 1: Make immediate predictions ----\n",
"exp.predict_model(final_model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1961-01 450.3183\n",
"1961-02 425.8193\n",
"1961-03 476.2090\n",
"1961-04 506.2128\n",
"1961-05 519.3201\n",
"1961-06 589.8758\n",
"1961-07 681.0200\n",
"1961-08 672.9888\n",
"1961-09 564.2336\n",
"1961-10 505.6469\n",
"1961-11 434.0557\n",
"1961-12 484.2149\n",
"Freq: M, Name: Number of airline passengers, dtype: float64"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2DoAHajo79qK",
"outputId": "2c85ba1b-e1f6-45bf-c2cb-77dce32190d2"
},
"source": [
"#### Option 2: Just save the model now and load it later for predictions ----\n",
"# Saves a pkl file\n",
"exp.save_model(final_model, \"my_best_model\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Transformation Pipeline and Model Successfully Saved\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(ExponentialSmoothing(damped_trend=False, initial_level=None,\n",
" initial_seasonal=None, initial_trend=None,\n",
" initialization_method='estimated', seasonal='add', sp=12,\n",
" trend='add', use_boxcox=True), 'my_best_model.pkl')"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
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"id": "SUkJFVHK8N4Y",
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"source": [
"# Then later, load this model in a different environment and make predictions\n",
"exp_l = TimeSeriesExperiment()\n",
"loaded_model = exp_l.load_model(\"my_best_model\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Transformation Pipeline and Model Successfully Loaded\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "HaEYpTkx8c1F",
"outputId": "11f5968d-095c-4e00-c8be-75ce388e6819"
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"source": [
"exp_l.predict_model(loaded_model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1961-01 450.3183\n",
"1961-02 425.8193\n",
"1961-03 476.2090\n",
"1961-04 506.2128\n",
"1961-05 519.3201\n",
"1961-06 589.8758\n",
"1961-07 681.0200\n",
"1961-08 672.9888\n",
"1961-09 564.2336\n",
"1961-10 505.6469\n",
"1961-11 434.0557\n",
"1961-12 484.2149\n",
"Freq: M, dtype: float64"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yER3tjaH8jRS"
},
"source": [
"Happy forecasting!"
]
}
]
}
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