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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ngupta23/12dc80fc39f0a57ac9b7416953114ece/pycaret_ts_enfore_pi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "v6fp55LVnX4J"
},
"source": [
"!pip install pycaret-ts-alpha -U\n",
"!pip install prophet"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 146
},
"id": "rAn-yHEen05j",
"outputId": "2af52587-4c85-4e7b-ea83-b44f19e916a7"
},
"source": [
"#### Load data ----\n",
"from pycaret.datasets import get_data\n",
"y = get_data(\"airline\")"
],
"execution_count": 2,
"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": {}
}
]
},
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},
"id": "uVBx_uqBn2d-",
"outputId": "923a79db-fe74-4e4b-ecfd-a8474ba6c150"
},
"source": [
"#### Setup experiment ----\n",
"from pycaret.internal.pycaret_experiment import TimeSeriesExperiment\n",
"exp = TimeSeriesExperiment()\n",
"\n",
"#### Enforce usage of only those models that have a prediction interval ----\n",
"exp.setup(data=y, fh=12, enforce_pi=True, session_id=42)"
],
"execution_count": 3,
"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>True</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>9610</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 True\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 9610\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f1a6f6b06d0>"
]
},
"metadata": {},
"execution_count": 3
}
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"id": "D0RcW8sCn8ZR",
"outputId": "4dccf8fd-6e77-4342-b6d5-29e9fa595d6a"
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"source": [
"# Available Models (only those having ability to generate prediction intervals) ----\n",
"exp.models()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Reference</th>\n",
" <th>Turbo</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>arima</th>\n",
" <td>ARIMA</td>\n",
" <td>sktime.forecasting.arima.ARIMA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>auto_arima</th>\n",
" <td>Auto ARIMA</td>\n",
" <td>sktime.forecasting.arima.AutoARIMA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ets</th>\n",
" <td>ETS</td>\n",
" <td>sktime.forecasting.ets.AutoETS</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>theta</th>\n",
" <td>Theta Forecaster</td>\n",
" <td>sktime.forecasting.theta.ThetaForecaster</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tbats</th>\n",
" <td>TBATS</td>\n",
" <td>sktime.forecasting.tbats.TBATS</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bats</th>\n",
" <td>BATS</td>\n",
" <td>sktime.forecasting.bats.BATS</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prophet</th>\n",
" <td>Prophet</td>\n",
" <td>pycaret.containers.models.time_series.ProphetP...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name ... Turbo\n",
"ID ... \n",
"arima ARIMA ... True\n",
"auto_arima Auto ARIMA ... True\n",
"ets ETS ... True\n",
"theta Theta Forecaster ... True\n",
"tbats TBATS ... False\n",
"bats BATS ... False\n",
"prophet Prophet ... False\n",
"\n",
"[7 rows x 3 columns]"
]
},
"metadata": {},
"execution_count": 4
}
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"id": "gB4-ueGmoJHd",
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"source": [
"#### Create Model & plot predictions ----\n",
"model = exp.create_model(\"ets\")\n",
"exp.plot_model(model)"
],
"execution_count": 5,
"outputs": [
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" <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>14.5727</td>\n",
" <td>18.7858</td>\n",
" <td>0.0367</td>\n",
" <td>0.0377</td>\n",
" <td>0.8851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>16.7941</td>\n",
" <td>19.3204</td>\n",
" <td>0.0458</td>\n",
" <td>0.0446</td>\n",
" <td>0.9022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>20.8905</td>\n",
" <td>23.4314</td>\n",
" <td>0.0495</td>\n",
" <td>0.0512</td>\n",
" <td>0.8772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>17.4191</td>\n",
" <td>20.5125</td>\n",
" <td>0.0440</td>\n",
" <td>0.0445</td>\n",
" <td>0.8882</td>\n",
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" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>2.6168</td>\n",
" <td>2.0755</td>\n",
" <td>0.0054</td>\n",
" <td>0.0055</td>\n",
" <td>0.0104</td>\n",
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"</table>\n",
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],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 14.5727 18.7858 0.0367 0.0377 0.8851\n",
"1 1957-12 16.7941 19.3204 0.0458 0.0446 0.9022\n",
"2 1958-12 20.8905 23.4314 0.0495 0.0512 0.8772\n",
"Mean NaN 17.4191 20.5125 0.0440 0.0445 0.8882\n",
"SD NaN 2.6168 2.0755 0.0054 0.0055 0.0104"
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{
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"id": "gTkJ4gvboyif",
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"source": [
"#### Get Predictions (with prediction interval) ----\n",
"exp.predict_model(model, return_pred_int=True)"
],
"execution_count": 6,
"outputs": [
{
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" Model MAE RMSE MAPE SMAPE R2\n",
"0 ETS 9.5289 15.4601 0.0203 0.0199 0.9569"
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" .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>y_pred</th>\n",
" <th>lower</th>\n",
" <th>upper</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1960-01</th>\n",
" <td>417.2265</td>\n",
" <td>399.1724</td>\n",
" <td>435.9598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-02</th>\n",
" <td>394.0018</td>\n",
" <td>374.2371</td>\n",
" <td>415.3192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-03</th>\n",
" <td>462.2049</td>\n",
" <td>441.7475</td>\n",
" <td>484.8106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-04</th>\n",
" <td>448.3367</td>\n",
" <td>424.8850</td>\n",
" <td>472.5015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-05</th>\n",
" <td>471.6366</td>\n",
" <td>445.9866</td>\n",
" <td>495.2967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-06</th>\n",
" <td>539.3359</td>\n",
" <td>512.4318</td>\n",
" <td>565.8552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-07</th>\n",
" <td>623.5610</td>\n",
" <td>593.5584</td>\n",
" <td>652.9493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-08</th>\n",
" <td>630.7469</td>\n",
" <td>597.6323</td>\n",
" <td>664.4028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-09</th>\n",
" <td>515.2000</td>\n",
" <td>487.5193</td>\n",
" <td>545.2246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-10</th>\n",
" <td>449.7405</td>\n",
" <td>423.1288</td>\n",
" <td>475.8882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-11</th>\n",
" <td>394.1071</td>\n",
" <td>368.6979</td>\n",
" <td>419.5679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1960-12</th>\n",
" <td>433.6767</td>\n",
" <td>405.3603</td>\n",
" <td>461.5141</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" y_pred lower upper\n",
"1960-01 417.2265 399.1724 435.9598\n",
"1960-02 394.0018 374.2371 415.3192\n",
"1960-03 462.2049 441.7475 484.8106\n",
"1960-04 448.3367 424.8850 472.5015\n",
"1960-05 471.6366 445.9866 495.2967\n",
"1960-06 539.3359 512.4318 565.8552\n",
"1960-07 623.5610 593.5584 652.9493\n",
"1960-08 630.7469 597.6323 664.4028\n",
"1960-09 515.2000 487.5193 545.2246\n",
"1960-10 449.7405 423.1288 475.8882\n",
"1960-11 394.1071 368.6979 419.5679\n",
"1960-12 433.6767 405.3603 461.5141"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269,
"referenced_widgets": [
"8a091b5ddaaf46e6bb4d839a564f1974",
"43c33e71a37448de8b37c27713772f1a",
"8e67598e2a52408fb55fe146275caea7"
]
},
"id": "WMrkrKf8pFS5",
"outputId": "86594097-0574-4079-c1ad-9568284746bc"
},
"source": [
"#### Compare only those models that have a prediction interval ----\n",
"best = exp.compare_models(turbo=False)"
],
"execution_count": 7,
"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>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>0.2800</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>0.0833</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>4.8833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tbats</th>\n",
" <td>TBATS</td>\n",
" <td>23.8716</td>\n",
" <td>28.4979</td>\n",
" <td>0.0586</td>\n",
" <td>0.0593</td>\n",
" <td>0.7707</td>\n",
" <td>33.1533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bats</th>\n",
" <td>BATS</td>\n",
" <td>27.9542</td>\n",
" <td>33.2127</td>\n",
" <td>0.0651</td>\n",
" <td>0.0685</td>\n",
" <td>0.6532</td>\n",
" <td>12.7967</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>0.0267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prophet</th>\n",
" <td>Prophet</td>\n",
" <td>30.239</td>\n",
" <td>35.6676</td>\n",
" <td>0.0782</td>\n",
" <td>0.0764</td>\n",
" <td>0.654</td>\n",
" <td>1.3500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE RMSE MAPE SMAPE R2 \\\n",
"ets ETS 17.4191 20.5125 0.044 0.0445 0.8882 \n",
"arima ARIMA 20.0069 22.2199 0.0501 0.0507 0.8677 \n",
"auto_arima Auto ARIMA 21.0297 23.4661 0.0525 0.0531 0.8509 \n",
"tbats TBATS 23.8716 28.4979 0.0586 0.0593 0.7707 \n",
"bats BATS 27.9542 33.2127 0.0651 0.0685 0.6532 \n",
"theta Theta Forecaster 28.3192 33.8639 0.067 0.07 0.671 \n",
"prophet Prophet 30.239 35.6676 0.0782 0.0764 0.654 \n",
"\n",
" TT (Sec) \n",
"ets 0.2800 \n",
"arima 0.0833 \n",
"auto_arima 4.8833 \n",
"tbats 33.1533 \n",
"bats 12.7967 \n",
"theta 0.0267 \n",
"prophet 1.3500 "
]
},
"metadata": {}
}
]
}
]
}
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