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"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"bar_color": null,
"_model_module": "@jupyter-widgets/controls"
}
},
"9668b468585140beb06f4186c95b1d16": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
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"width": null,
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"border": null,
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"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
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"grid_auto_flow": null,
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"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
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"padding": null,
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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/f94b2f3ce22b7a0203446b77f2972ffa/pycaret_ts_compare_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UthgOegeztkM",
"outputId": "bc752c92-7c9e-4a00-efc9-7618f8e63feb"
},
"source": [
"!pip install pycaret-ts-alpha -U\n",
"!pip install prophet"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 146
},
"id": "HgFuWQ5v0KWk",
"outputId": "311d1634-9306-4f08-90ea-47aa8259d1df"
},
"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": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 695,
"referenced_widgets": [
"62f2610cf5714f20a6e34720d1a2beac",
"da3f721c98ec4e4b9667f9a4d699672b",
"d6da67a582cd4cf1913db62cdde39080"
]
},
"id": "_YraLh_21Ev0",
"outputId": "e512e86b-d3a5-43f6-a40a-8b43907bf200"
},
"source": [
"#### Setup experiment ----\n",
"from pycaret.internal.pycaret_experiment.time_series_experiment import TimeSeriesExperiment\n",
"exp = TimeSeriesExperiment()\n",
"\n",
"exp.setup(data=y, fh=12, 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>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>0099</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 0099\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f65c096a790>"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 959
},
"id": "rd2THcDQ0_XK",
"outputId": "e9424792-e35a-4ded-d523-d004af1acd5b"
},
"source": [
"#### Available Models\n",
"exp.models()"
],
"execution_count": 4,
"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>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>naive</th>\n",
" <td>Naive Forecaster</td>\n",
" <td>sktime.forecasting.naive.NaiveForecaster</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snaive</th>\n",
" <td>Seasonal Naive Forecaster</td>\n",
" <td>sktime.forecasting.naive.NaiveForecaster</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>polytrend</th>\n",
" <td>Polynomial Trend Forecaster</td>\n",
" <td>sktime.forecasting.trend.PolynomialTrendForeca...</td>\n",
" <td>True</td>\n",
" </tr>\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>exp_smooth</th>\n",
" <td>Exponential Smoothing</td>\n",
" <td>sktime.forecasting.exp_smoothing.ExponentialSm...</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",
" <tr>\n",
" <th>lr_cds_dt</th>\n",
" <td>Linear w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>en_cds_dt</th>\n",
" <td>Elastic Net w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ridge_cds_dt</th>\n",
" <td>Ridge w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lasso_cds_dt</th>\n",
" <td>Lasso w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lar_cds_dt</th>\n",
" <td>Least Angular Regressor w/ Cond. Deseasonalize...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>llar_cds_dt</th>\n",
" <td>Lasso Least Angular Regressor w/ Cond. Deseaso...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>br_cds_dt</th>\n",
" <td>Bayesian Ridge w/ Cond. Deseasonalize &amp; Detren...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>huber_cds_dt</th>\n",
" <td>Huber w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>par_cds_dt</th>\n",
" <td>Passive Aggressive w/ Cond. Deseasonalize &amp; De...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>omp_cds_dt</th>\n",
" <td>Orthogonal Matching Pursuit w/ Cond. Deseasona...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>knn_cds_dt</th>\n",
" <td>K Neighbors w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dt_cds_dt</th>\n",
" <td>Decision Tree w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rf_cds_dt</th>\n",
" <td>Random Forest w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>et_cds_dt</th>\n",
" <td>Extra Trees w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gbr_cds_dt</th>\n",
" <td>Gradient Boosting w/ Cond. Deseasonalize &amp; Det...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ada_cds_dt</th>\n",
" <td>AdaBoost w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lightgbm_cds_dt</th>\n",
" <td>Light Gradient Boosting w/ Cond. Deseasonalize...</td>\n",
" <td>pycaret.containers.models.time_series.BaseCdsD...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name ... Turbo\n",
"ID ... \n",
"naive Naive Forecaster ... True\n",
"snaive Seasonal Naive Forecaster ... True\n",
"polytrend Polynomial Trend Forecaster ... True\n",
"arima ARIMA ... True\n",
"auto_arima Auto ARIMA ... True\n",
"exp_smooth Exponential Smoothing ... True\n",
"ets ETS ... True\n",
"theta Theta Forecaster ... True\n",
"tbats TBATS ... False\n",
"bats BATS ... False\n",
"prophet Prophet ... False\n",
"lr_cds_dt Linear w/ Cond. Deseasonalize & Detrending ... True\n",
"en_cds_dt Elastic Net w/ Cond. Deseasonalize & Detrending ... True\n",
"ridge_cds_dt Ridge w/ Cond. Deseasonalize & Detrending ... True\n",
"lasso_cds_dt Lasso w/ Cond. Deseasonalize & Detrending ... True\n",
"lar_cds_dt Least Angular Regressor w/ Cond. Deseasonalize... ... True\n",
"llar_cds_dt Lasso Least Angular Regressor w/ Cond. Deseaso... ... True\n",
"br_cds_dt Bayesian Ridge w/ Cond. Deseasonalize & Detren... ... True\n",
"huber_cds_dt Huber w/ Cond. Deseasonalize & Detrending ... True\n",
"par_cds_dt Passive Aggressive w/ Cond. Deseasonalize & De... ... True\n",
"omp_cds_dt Orthogonal Matching Pursuit w/ Cond. Deseasona... ... True\n",
"knn_cds_dt K Neighbors w/ Cond. Deseasonalize & Detrending ... True\n",
"dt_cds_dt Decision Tree w/ Cond. Deseasonalize & Detrending ... True\n",
"rf_cds_dt Random Forest w/ Cond. Deseasonalize & Detrending ... True\n",
"et_cds_dt Extra Trees w/ Cond. Deseasonalize & Detrending ... True\n",
"gbr_cds_dt Gradient Boosting w/ Cond. Deseasonalize & Det... ... True\n",
"ada_cds_dt AdaBoost w/ Cond. Deseasonalize & Detrending ... True\n",
"lightgbm_cds_dt Light Gradient Boosting w/ Cond. Deseasonalize... ... True\n",
"\n",
"[28 rows x 3 columns]"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N5_qURrVABwi"
},
"source": [
"## Manually Flow"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nZ1TJQPz1OKB"
},
"source": [
"Which ones should you try?\n",
"\n",
"Recommendation: Always start from a simple baseline model ..."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
"17a8d2dc2a384df894af1a544d36e207",
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"6c1579206380405f827794720233e3f0"
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"id": "54DgwWsy1C1x",
"outputId": "1eaef46b-73a8-4c24-ac13-927e4617c5e4"
},
"source": [
"# Seasonal Naive Baseline ----\n",
"baseline = exp.create_model(\"snaive\")\n",
"exp.plot_model(baseline)"
],
"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",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>40.1667</td>\n",
" <td>41.4749</td>\n",
" <td>0.1076</td>\n",
" <td>0.1138</td>\n",
" <td>0.4401</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>12.5833</td>\n",
" <td>17.0123</td>\n",
" <td>0.0314</td>\n",
" <td>0.0322</td>\n",
" <td>0.9242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>47.3333</td>\n",
" <td>49.2544</td>\n",
" <td>0.1106</td>\n",
" <td>0.1176</td>\n",
" <td>0.4573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>14.9806</td>\n",
" <td>13.7376</td>\n",
" <td>0.0367</td>\n",
" <td>0.0394</td>\n",
" <td>0.2243</td>\n",
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"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 40.1667 41.4749 0.1076 0.1138 0.4401\n",
"1 1957-12 12.5833 17.0123 0.0314 0.0322 0.9242\n",
"2 1958-12 47.3333 49.2544 0.1106 0.1176 0.4573\n",
"Mean NaN 33.3611 35.9139 0.0832 0.0879 0.6072\n",
"SD NaN 14.9806 13.7376 0.0367 0.0394 0.2243"
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"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; </script> </div>\n",
"</body>\n",
"</html>"
]
},
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}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dG_W70kz1jY3"
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"source": [
"OK, captures seasonality but can we do better?\n",
"\n",
"Let's try a few more models to see..."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
"ef752d6a71cd4bd8ba0950e770bf5723"
]
},
"id": "dmiO-rWu1eNk",
"outputId": "3e1273eb-677f-4137-8f4e-b191d9d8c68c"
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"source": [
"#### Classical Statistical Model: ARIMA\n",
"model1 = exp.create_model(\"arima\")\n",
"exp.plot_model(model1)"
],
"execution_count": 6,
"outputs": [
{
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" <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>13.0286</td>\n",
" <td>16.1485</td>\n",
" <td>0.0327</td>\n",
" <td>0.0334</td>\n",
" <td>0.9151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>18.2920</td>\n",
" <td>20.3442</td>\n",
" <td>0.0506</td>\n",
" <td>0.0491</td>\n",
" <td>0.8916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>28.6999</td>\n",
" <td>30.1669</td>\n",
" <td>0.0671</td>\n",
" <td>0.0697</td>\n",
" <td>0.7964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>6.5117</td>\n",
" <td>5.8746</td>\n",
" <td>0.0141</td>\n",
" <td>0.0148</td>\n",
" <td>0.0513</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 13.0286 16.1485 0.0327 0.0334 0.9151\n",
"1 1957-12 18.2920 20.3442 0.0506 0.0491 0.8916\n",
"2 1958-12 28.6999 30.1669 0.0671 0.0697 0.7964\n",
"Mean NaN 20.0069 22.2199 0.0501 0.0507 0.8677\n",
"SD NaN 6.5117 5.8746 0.0141 0.0148 0.0513"
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"#### Classical Statistical Model: ETS\n",
"model2 = exp.create_model(\"ets\")\n",
"exp.plot_model(model2)"
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"execution_count": 7,
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" <td>1956-12</td>\n",
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" <td>20.8905</td>\n",
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" <td>17.4191</td>\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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"id": "IKDmYmDX16na",
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"source": [
"#### Regression Based Model with Lags: lightgbm\n",
"model3 = exp.create_model(\"lightgbm_cds_dt\")\n",
"exp.plot_model(model3)"
],
"execution_count": 8,
"outputs": [
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" <th>MAE</th>\n",
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" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>36.4896</td>\n",
" <td>43.0267</td>\n",
" <td>0.0939</td>\n",
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" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>25.4164</td>\n",
" <td>30.6510</td>\n",
" <td>0.0641</td>\n",
" <td>0.0642</td>\n",
" <td>0.7539</td>\n",
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" <th>2</th>\n",
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" <td>24.4155</td>\n",
" <td>35.0399</td>\n",
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" <td>28.7738</td>\n",
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" <td>5.4712</td>\n",
" <td>5.1231</td>\n",
" <td>0.0178</td>\n",
" <td>0.0191</td>\n",
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"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 36.4896 43.0267 0.0939 0.0986 0.3974\n",
"1 1957-12 25.4164 30.6510 0.0641 0.0642 0.7539\n",
"2 1958-12 24.4155 35.0399 0.0514 0.0539 0.7253\n",
"Mean NaN 28.7738 36.2392 0.0698 0.0722 0.6255\n",
"SD NaN 5.4712 5.1231 0.0178 0.0191 0.1617"
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"source": [
"## Automated Comparison"
]
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"source": [
"### Basics"
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"So some models do better than others. How do I know which one forecasts the best? Trying one by one would be time comsuming. \n",
"\n",
"That's why `pycaret` provides a convenient wrapper to do this - `compare_models`. Let's see how it works. "
]
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"best_baseline = exp.compare_models()\n",
"exp.plot_model(best_baseline)"
],
"execution_count": 9,
"outputs": [
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" <th>et_cds_dt</th>\n",
" <td>Extra Trees w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>24.4233</td>\n",
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" <td>25.8293</td>\n",
" <td>34.9633</td>\n",
" <td>0.0617</td>\n",
" <td>0.0641</td>\n",
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" <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>0.1167</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>0.0700</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>0.0367</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>0.0233</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>0.0267</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>0.0200</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>0.0233</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>0.0467</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>0.0233</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>0.0233</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>0.0233</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>0.0133</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 0.1400 \n",
"ets 20.5125 0.044 0.0445 0.8882 0.2033 \n",
"arima 22.2199 0.0501 0.0507 0.8677 0.0767 \n",
"auto_arima 23.4661 0.0525 0.0531 0.8509 4.3500 \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 1.0067 \n",
"ada_cds_dt 37.791 0.0661 0.0686 0.6015 0.1167 \n",
"theta 33.8639 0.067 0.07 0.671 0.0200 \n",
"gbr_cds_dt 38.308 0.0685 0.0715 0.5855 0.0700 \n",
"lightgbm_cds_dt 36.2392 0.0698 0.0722 0.6255 0.0367 \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 0.0233 \n",
"en_cds_dt 39.2557 0.0825 0.0843 0.5669 0.0267 \n",
"lr_cds_dt 39.3456 0.0828 0.0846 0.5652 0.0200 \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 0.0233 \n",
"snaive 35.9139 0.0832 0.0879 0.6072 0.0167 \n",
"huber_cds_dt 41.1489 0.091 0.0936 0.5226 0.0467 \n",
"lar_cds_dt 42.4001 0.0936 0.0945 0.5058 0.0233 \n",
"llar_cds_dt 63.1706 0.1109 0.1165 -0.0733 0.0233 \n",
"omp_cds_dt 64.5891 0.111 0.1177 -0.1201 0.0233 \n",
"polytrend 63.4299 0.117 0.1216 -0.0784 0.0133 \n",
"naive 91.0322 0.1569 0.1792 -1.2216 0.0133 \n",
"par_cds_dt 95.4251 0.2137 0.2531 -3.0784 0.0233 "
]
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"We are now able to quickly compare multiple models using cross-validation and get the best model(s) for the next steps of our modeling flow.\n",
"\n",
"`compare_models` by default, returns the best model from the list. Sometimes, however, there is also a need for get the top N models for further analysis. `compare_modls` provides a conventient way to do this as well."
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"#### Return the 3 top models ----\n",
"best_baseline_models = exp.compare_models(n_select=3)\n",
"for model in best_baseline_models:\n",
" exp.plot_model(model)"
],
"execution_count": 10,
"outputs": [
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" <th>et_cds_dt</th>\n",
" <td>Extra Trees w/ Cond. Deseasonalize &amp; Detrending</td>\n",
" <td>24.4233</td>\n",
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" <td>0.0617</td>\n",
" <td>0.0641</td>\n",
" <td>0.626</td>\n",
" <td>0.8367</td>\n",
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" <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>0.0167</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>0.0700</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>0.0367</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>0.0233</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>0.0233</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>0.0267</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>0.0233</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>0.0467</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>0.0233</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>0.0233</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>0.0233</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>0.0100</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>0.0167</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 0.1333 \n",
"ets 20.5125 0.044 0.0445 0.8882 0.2100 \n",
"arima 22.2199 0.0501 0.0507 0.8677 0.0700 \n",
"auto_arima 23.4661 0.0525 0.0531 0.8509 4.2800 \n",
"et_cds_dt 31.4395 0.0584 0.0601 0.7169 0.9733 \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 1.0100 \n",
"ada_cds_dt 37.791 0.0661 0.0686 0.6015 0.1200 \n",
"theta 33.8639 0.067 0.07 0.671 0.0167 \n",
"gbr_cds_dt 38.308 0.0685 0.0715 0.5855 0.0700 \n",
"lightgbm_cds_dt 36.2392 0.0698 0.0722 0.6255 0.0367 \n",
"br_cds_dt 39.2191 0.0799 0.0818 0.5658 0.0233 \n",
"lasso_cds_dt 39.2084 0.0823 0.0841 0.5678 0.0233 \n",
"en_cds_dt 39.2557 0.0825 0.0843 0.5669 0.0267 \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 0.0233 \n",
"snaive 35.9139 0.0832 0.0879 0.6072 0.0167 \n",
"huber_cds_dt 41.1489 0.091 0.0936 0.5226 0.0467 \n",
"lar_cds_dt 42.4001 0.0936 0.0945 0.5058 0.0233 \n",
"llar_cds_dt 63.1706 0.1109 0.1165 -0.0733 0.0233 \n",
"omp_cds_dt 64.5891 0.111 0.1177 -0.1201 0.0233 \n",
"polytrend 63.4299 0.117 0.1216 -0.0784 0.0100 \n",
"naive 91.0322 0.1569 0.1792 -1.2216 0.0167 \n",
"par_cds_dt 95.4251 0.2137 0.2531 -3.0784 0.0233 "
]
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"source": [
"### Controlling the Run Time"
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"By default, `compare_models` will only run fast models. Slower models such as `bats`, `tbats`, `prophet` are not run by default. We can enable these models by setting `turbo=False` in the `compare_models` call."
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"all_baseline_inc_slow = exp.compare_models(turbo=False)"
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" <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>1.0067</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>10.4600</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>0.1167</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>0.0700</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>0.0367</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>2.1567</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>0.0233</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>0.0267</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>0.7800</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>0.0400</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>0.0300</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>0.0233</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>0.0233</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>0.0100</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>0.0133</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>0.0300</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",
"tbats TBATS 23.8716 \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",
"bats BATS 27.9542 \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",
"prophet Prophet 30.239 \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 0.1367 \n",
"ets 20.5125 0.044 0.0445 0.8882 0.2033 \n",
"arima 22.2199 0.0501 0.0507 0.8677 0.0767 \n",
"auto_arima 23.4661 0.0525 0.0531 0.8509 4.2933 \n",
"tbats 28.4979 0.0586 0.0593 0.7707 26.4000 \n",
"et_cds_dt 31.4395 0.0584 0.0601 0.7169 0.9733 \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 1.0067 \n",
"bats 33.2127 0.0651 0.0685 0.6532 10.4600 \n",
"ada_cds_dt 37.791 0.0661 0.0686 0.6015 0.1167 \n",
"theta 33.8639 0.067 0.07 0.671 0.0200 \n",
"gbr_cds_dt 38.308 0.0685 0.0715 0.5855 0.0700 \n",
"lightgbm_cds_dt 36.2392 0.0698 0.0722 0.6255 0.0367 \n",
"prophet 35.6676 0.0782 0.0764 0.654 2.1567 \n",
"br_cds_dt 39.2191 0.0799 0.0818 0.5658 0.0233 \n",
"lasso_cds_dt 39.2084 0.0823 0.0841 0.5678 0.0267 \n",
"en_cds_dt 39.2557 0.0825 0.0843 0.5669 0.7800 \n",
"lr_cds_dt 39.3456 0.0828 0.0846 0.5652 0.0400 \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 0.0300 \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 0.0233 \n",
"omp_cds_dt 64.5891 0.111 0.1177 -0.1201 0.0233 \n",
"polytrend 63.4299 0.117 0.1216 -0.0784 0.0100 \n",
"naive 91.0322 0.1569 0.1792 -1.2216 0.0133 \n",
"par_cds_dt 95.4251 0.2137 0.2531 -3.0784 0.0300 "
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OHZgcTCi9eEO"
},
"source": [
"Sometimes, you may want to limit the run based on a budgeted time. This is especially helpful when you have to scale to 100's and 1000's of time series but only have a limited time to cerate the models. This can be done easily using the `budget_time` argument"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206,
"referenced_widgets": [
"c59627b19ec34adfb96686cb917f6a45",
"d317616e7315492a811c5cc070a1d11b",
"2907e39d613b4a718e05feeb4bc293a4"
]
},
"id": "bjRzz79u26o6",
"outputId": "74217c1c-604a-4a0e-f2b5-12d463c67ca0"
},
"source": [
"all_baseline_some_models = exp.compare_models(turbo=False, budget_time=0.1)"
],
"execution_count": 12,
"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>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>0.0733</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>4.3100</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>0.0133</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>0.0100</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>0.0133</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE RMSE MAPE SMAPE \\\n",
"arima ARIMA 20.0069 22.2199 0.0501 0.0507 \n",
"auto_arima Auto ARIMA 21.0297 23.4661 0.0525 0.0531 \n",
"snaive Seasonal Naive Forecaster 33.3611 35.9139 0.0832 0.0879 \n",
"polytrend Polynomial Trend Forecaster 48.6301 63.4299 0.117 0.1216 \n",
"naive Naive Forecaster 69.0278 91.0322 0.1569 0.1792 \n",
"\n",
" R2 TT (Sec) \n",
"arima 0.8677 0.0733 \n",
"auto_arima 0.8509 4.3100 \n",
"snaive 0.6072 0.0133 \n",
"polytrend -0.0784 0.0100 \n",
"naive -1.2216 0.0133 "
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u84K3R1KAw7t"
},
"source": [
"### Including and Excluding Models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gbGzRO-d8k7i"
},
"source": [
"Sometimes, there may be a need to run only certain models or exclude certain models from the run. This can be done using the `include` and `exclude` arguments respectively. Let's see how we can only include `prophet` and exclude `bats` and `tbats` from the previous run. "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 865,
"referenced_widgets": [
"aa261a1a1718436eb0bc7ce4554b4d51",
"e1e2fe4e74b940bfa2c3ca6ae5d6dcc9",
"9668b468585140beb06f4186c95b1d16"
]
},
"id": "QJoBWOSt85PZ",
"outputId": "17050db2-53da-47f2-a26a-0d8c8746511d"
},
"source": [
"all_baseline_some_models = exp.compare_models(turbo=False, exclude=['bats', 'tbats'])"
],
"execution_count": 13,
"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>0.1400</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>0.2033</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>0.0700</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>4.2967</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>0.8400</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>1.0067</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>0.1167</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>0.0667</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>0.0367</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>0.7667</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>0.0233</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>0.0233</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>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>0.0267</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>0.8867</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>0.0400</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>0.0233</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>0.0233</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>0.0167</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>0.0200</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",
"prophet Prophet 30.239 \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",
"ridge_cds_dt Ridge w/ Cond. Deseasonalize & Detrending 32.9702 \n",
"lr_cds_dt Linear w/ Cond. Deseasonalize & Detrending 32.9708 \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 0.1400 \n",
"ets 20.5125 0.044 0.0445 0.8882 0.2033 \n",
"arima 22.2199 0.0501 0.0507 0.8677 0.0700 \n",
"auto_arima 23.4661 0.0525 0.0531 0.8509 4.2967 \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 0.8400 \n",
"rf_cds_dt 38.0499 0.0629 0.0654 0.6023 1.0067 \n",
"ada_cds_dt 37.791 0.0661 0.0686 0.6015 0.1167 \n",
"theta 33.8639 0.067 0.07 0.671 0.0200 \n",
"gbr_cds_dt 38.308 0.0685 0.0715 0.5855 0.0667 \n",
"lightgbm_cds_dt 36.2392 0.0698 0.0722 0.6255 0.0367 \n",
"prophet 35.6676 0.0782 0.0764 0.654 0.7667 \n",
"br_cds_dt 39.2191 0.0799 0.0818 0.5658 0.0233 \n",
"lasso_cds_dt 39.2084 0.0823 0.0841 0.5678 0.0233 \n",
"en_cds_dt 39.2557 0.0825 0.0843 0.5669 0.0233 \n",
"ridge_cds_dt 39.3452 0.0828 0.0846 0.5652 0.0267 \n",
"lr_cds_dt 39.3456 0.0828 0.0846 0.5652 0.8867 \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 0.0400 \n",
"lar_cds_dt 42.4001 0.0936 0.0945 0.5058 0.0233 \n",
"llar_cds_dt 63.1706 0.1109 0.1165 -0.0733 0.0233 \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 0.0167 \n",
"par_cds_dt 95.4251 0.2137 0.2531 -3.0784 0.0200 "
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rU7kokiW9R6-"
},
"source": [
""
],
"execution_count": 13,
"outputs": []
}
]
}
@biomedatascientistSV
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@ngupta23 works like a charm, beautiful!!

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