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"source": [
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],
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"text": [
"Requirement already satisfied: pycaret-ts-alpha in /usr/local/lib/python3.7/dist-packages (3.0.0.dev1635609350)\n",
"Requirement already satisfied: gensim<4.0.0 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (3.6.0)\n",
"Requirement already satisfied: scikit-learn~=0.24.2 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.24.2)\n",
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"Requirement already satisfied: cufflinks>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.17.3)\n",
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]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 146
},
"id": "G91sw_lDJiOM",
"outputId": "fe6fd4ec-43d8-40b0-c577-2f038819cf7e"
},
"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": [
"adb7060989e84bf79d76f0621182ceff",
"35f925cd2a224a2a95cd9e920ea2ec70",
"e15c83c8b9984d3c825b86faf5267ddc"
]
},
"id": "_j18fGv6KNXw",
"outputId": "924466a8-dee8-4eb4-d507-b399b60bfc86"
},
"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>2cca</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 2cca\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f6d3b61d910>"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XFBU8y4K2EeJ"
},
"source": [
"# Part 1: Basic Tuning\n",
"\n",
"## Baseline Model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
"2984e020d89f4e39a09d7a5284e07fac",
"e61e651987f34f13a13ea321fec44665",
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},
"id": "PNOu9y8zJwa2",
"outputId": "5427149c-b922-4d3b-b0de-7443403fac03"
},
"source": [
"model = exp.create_model(\"lr_cds_dt\")\n",
"exp.plot_model(model)"
],
"execution_count": 4,
"outputs": [
{
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" <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>38.6824</td>\n",
" <td>45.0820</td>\n",
" <td>0.0998</td>\n",
" <td>0.1051</td>\n",
" <td>0.3384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>28.0608</td>\n",
" <td>34.6867</td>\n",
" <td>0.0751</td>\n",
" <td>0.0734</td>\n",
" <td>0.6848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>32.1693</td>\n",
" <td>38.2681</td>\n",
" <td>0.0737</td>\n",
" <td>0.0753</td>\n",
" <td>0.6724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>4.3731</td>\n",
" <td>4.3117</td>\n",
" <td>0.0120</td>\n",
" <td>0.0145</td>\n",
" <td>0.1604</td>\n",
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"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 38.6824 45.0820 0.0998 0.1051 0.3384\n",
"1 1957-12 28.0608 34.6867 0.0751 0.0734 0.6848\n",
"2 1958-12 32.1693 38.2681 0.0737 0.0753 0.6724\n",
"Mean NaN 32.9708 39.3456 0.0828 0.0846 0.5652\n",
"SD NaN 4.3731 4.3117 0.0120 0.0145 0.1604"
]
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},
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"cell_type": "code",
"metadata": {
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"base_uri": "https://localhost:8080/"
},
"id": "bjD1dtfOO0CN",
"outputId": "2da1711e-1f1d-4d86-d7ab-a9985784cf36"
},
"source": [
"#### What are the model hyperparameters? ----\n",
"model"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BaseCdsDtForecaster(degree=1, deseasonal_model='additive',\n",
" regressor=LinearRegression(copy_X=True, fit_intercept=True,\n",
" n_jobs=-1, normalize=False,\n",
" positive=False),\n",
" sp=1, window_length=10)"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-alpo8Td2HRo"
},
"source": [
"## Randon Grid Search"
]
},
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"colab": {
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"id": "2iqFHDigedfa",
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"source": [
"#### Tune the model (Default = Random Grid Search) ----\n",
"tuned_model_random = exp.tune_model(model)\n",
"exp.plot_model(tuned_model_random)"
],
"execution_count": 6,
"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",
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" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>9.2184</td>\n",
" <td>12.1077</td>\n",
" <td>0.0233</td>\n",
" <td>0.0235</td>\n",
" <td>0.9523</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>30.6011</td>\n",
" <td>33.3898</td>\n",
" <td>0.0834</td>\n",
" <td>0.0794</td>\n",
" <td>0.7079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>13.6786</td>\n",
" <td>15.8682</td>\n",
" <td>0.0320</td>\n",
" <td>0.0325</td>\n",
" <td>0.9437</td>\n",
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" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>17.8327</td>\n",
" <td>20.4552</td>\n",
" <td>0.0462</td>\n",
" <td>0.0452</td>\n",
" <td>0.8680</td>\n",
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" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>9.2104</td>\n",
" <td>9.2741</td>\n",
" <td>0.0265</td>\n",
" <td>0.0245</td>\n",
" <td>0.1132</td>\n",
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"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 9.2184 12.1077 0.0233 0.0235 0.9523\n",
"1 1957-12 30.6011 33.3898 0.0834 0.0794 0.7079\n",
"2 1958-12 13.6786 15.8682 0.0320 0.0325 0.9437\n",
"Mean NaN 17.8327 20.4552 0.0462 0.0452 0.8680\n",
"SD NaN 9.2104 9.2741 0.0265 0.0245 0.1132"
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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rOeKvrHh1qTO",
"outputId": "669f2c1a-96b4-4723-8ee6-e73fe02ce325"
},
"source": [
"#### What are the tuned model hyperparameters? ----\n",
"tuned_model_random"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BaseCdsDtForecaster(degree=2, deseasonal_model='multiplicative',\n",
" regressor=LinearRegression(copy_X=True, fit_intercept=False,\n",
" n_jobs=-1, normalize=True,\n",
" positive=False),\n",
" sp=12, window_length=23)"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iwZ41ruRKIOP",
"outputId": "b83a89ad-1928-4ba8-8c4b-ec49ce64a1f9"
},
"source": [
"#### OK, so what search space was used? ----\n",
"random_grid = exp.models(internal=True).loc[\"lr_cds_dt\", \"Tune Distributions\"]\n",
"random_grid"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'degree': IntUniformDistribution(lower=1, upper=10, log=False),\n",
" 'deseasonal_model': CategoricalDistribution(values=['additive', 'multiplicative']),\n",
" 'regressor__fit_intercept': CategoricalDistribution(values=[True, False]),\n",
" 'regressor__normalize': CategoricalDistribution(values=[True, False]),\n",
" 'sp': CategoricalDistribution(values=[12, 24]),\n",
" 'window_length': IntUniformDistribution(lower=12, upper=24, log=False)}"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gXfSvkXy2LcB"
},
"source": [
"## Fixed Grid Search"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
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"source": [
"#### Tune the model (Fixed Grid Search) ----\n",
"tuned_model_grid = exp.tune_model(model, search_algorithm = \"grid\")\n",
"exp.plot_model(tuned_model_grid)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
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" <th>cutoff</th>\n",
" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
" <th>R2</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>24.0975</td>\n",
" <td>32.7998</td>\n",
" <td>0.0587</td>\n",
" <td>0.0616</td>\n",
" <td>0.6498</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>21.7530</td>\n",
" <td>25.4177</td>\n",
" <td>0.0583</td>\n",
" <td>0.0571</td>\n",
" <td>0.8307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>20.5818</td>\n",
" <td>26.9684</td>\n",
" <td>0.0445</td>\n",
" <td>0.0459</td>\n",
" <td>0.8373</td>\n",
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" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>22.1441</td>\n",
" <td>28.3953</td>\n",
" <td>0.0539</td>\n",
" <td>0.0549</td>\n",
" <td>0.7726</td>\n",
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" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>1.4617</td>\n",
" <td>3.1781</td>\n",
" <td>0.0066</td>\n",
" <td>0.0066</td>\n",
" <td>0.0869</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 24.0975 32.7998 0.0587 0.0616 0.6498\n",
"1 1957-12 21.7530 25.4177 0.0583 0.0571 0.8307\n",
"2 1958-12 20.5818 26.9684 0.0445 0.0459 0.8373\n",
"Mean NaN 22.1441 28.3953 0.0539 0.0549 0.7726\n",
"SD NaN 1.4617 3.1781 0.0066 0.0066 0.0869"
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"id": "nTBQ3nqO1bQ1",
"outputId": "d4d68139-067c-4996-df69-1865e1c4760c"
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"source": [
"#### What search space was used? \n",
"fixed_grid = exp.models(internal=True).loc[\"lr_cds_dt\", \"Tune Grid\"]\n",
"fixed_grid"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'degree': [1],\n",
" 'deseasonal_model': ['additive'],\n",
" 'regressor__fit_intercept': [True, False],\n",
" 'regressor__normalize': [True, False],\n",
" 'sp': [12],\n",
" 'window_length': [10]}"
]
},
"metadata": {},
"execution_count": 10
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},
{
"cell_type": "markdown",
"metadata": {
"id": "Ujg8Afhf7XQC"
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"source": [
"# Part 2: Advanced Tuning & Customization"
]
},
{
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"metadata": {
"id": "d7N7taoV7GnS"
},
"source": [
"## Looking under the Hood"
]
},
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"source": [
"tuned_model_random, random_tuner = exp.tune_model(model, return_tuner=True)"
],
"execution_count": 11,
"outputs": [
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" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1956-12</td>\n",
" <td>9.2184</td>\n",
" <td>12.1077</td>\n",
" <td>0.0233</td>\n",
" <td>0.0235</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>30.6011</td>\n",
" <td>33.3898</td>\n",
" <td>0.0834</td>\n",
" <td>0.0794</td>\n",
" <td>0.7079</td>\n",
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" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>13.6786</td>\n",
" <td>15.8682</td>\n",
" <td>0.0320</td>\n",
" <td>0.0325</td>\n",
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" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>17.8327</td>\n",
" <td>20.4552</td>\n",
" <td>0.0462</td>\n",
" <td>0.0452</td>\n",
" <td>0.8680</td>\n",
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" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>9.2104</td>\n",
" <td>9.2741</td>\n",
" <td>0.0265</td>\n",
" <td>0.0245</td>\n",
" <td>0.1132</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 9.2184 12.1077 0.0233 0.0235 0.9523\n",
"1 1957-12 30.6011 33.3898 0.0834 0.0794 0.7079\n",
"2 1958-12 13.6786 15.8682 0.0320 0.0325 0.9437\n",
"Mean NaN 17.8327 20.4552 0.0462 0.0452 0.8680\n",
"SD NaN 9.2104 9.2741 0.0265 0.0245 0.1132"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ssaih1JQ7PYK",
"outputId": "a9edeac8-7c1f-4bff-9fe3-cc816199f7be"
},
"source": [
"tuned_model_random"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BaseCdsDtForecaster(degree=2, deseasonal_model='multiplicative',\n",
" regressor=LinearRegression(copy_X=True, fit_intercept=False,\n",
" n_jobs=-1, normalize=True,\n",
" positive=False),\n",
" sp=12, window_length=23)"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 730
},
"id": "0gs8eRgn7LFC",
"outputId": "032c41e9-51ff-4428-b21b-06e0f5aad784"
},
"source": [
"import pandas as pd\n",
"pd.DataFrame(random_tuner.cv_results_).sort_values(\"rank_test_smape\")"
],
"execution_count": 13,
"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>mean_fit_time</th>\n",
" <th>std_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>std_score_time</th>\n",
" <th>param_degree</th>\n",
" <th>param_deseasonal_model</th>\n",
" <th>param_regressor__fit_intercept</th>\n",
" <th>param_regressor__normalize</th>\n",
" <th>param_sp</th>\n",
" <th>param_window_length</th>\n",
" <th>params</th>\n",
" <th>split0_test_smape</th>\n",
" <th>split1_test_smape</th>\n",
" <th>split2_test_smape</th>\n",
" <th>mean_test_smape</th>\n",
" <th>std_test_smape</th>\n",
" <th>rank_test_smape</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.018198</td>\n",
" <td>0.000647</td>\n",
" <td>0.000513</td>\n",
" <td>0.000022</td>\n",
" <td>2</td>\n",
" <td>multiplicative</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>23</td>\n",
" <td>{'degree': 2, 'deseasonal_model': 'multiplicat...</td>\n",
" <td>0.023537</td>\n",
" <td>0.079412</td>\n",
" <td>0.032535</td>\n",
" <td>0.045161</td>\n",
" <td>0.024496</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.022529</td>\n",
" <td>0.005692</td>\n",
" <td>0.000531</td>\n",
" <td>0.000016</td>\n",
" <td>3</td>\n",
" <td>multiplicative</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>24</td>\n",
" <td>20</td>\n",
" <td>{'degree': 3, 'deseasonal_model': 'multiplicat...</td>\n",
" <td>0.013970</td>\n",
" <td>0.088033</td>\n",
" <td>0.058526</td>\n",
" <td>0.053510</td>\n",
" <td>0.030443</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.018850</td>\n",
" <td>0.001927</td>\n",
" <td>0.000535</td>\n",
" <td>0.000034</td>\n",
" <td>3</td>\n",
" <td>additive</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>20</td>\n",
" <td>{'degree': 3, 'deseasonal_model': 'additive', ...</td>\n",
" <td>0.025654</td>\n",
" <td>0.094477</td>\n",
" <td>0.056408</td>\n",
" <td>0.058846</td>\n",
" <td>0.028150</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.020537</td>\n",
" <td>0.003949</td>\n",
" <td>0.000508</td>\n",
" <td>0.000020</td>\n",
" <td>5</td>\n",
" <td>additive</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>{'degree': 5, 'deseasonal_model': 'additive', ...</td>\n",
" <td>0.128711</td>\n",
" <td>0.090898</td>\n",
" <td>0.276334</td>\n",
" <td>0.165315</td>\n",
" <td>0.080006</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.026006</td>\n",
" <td>0.011879</td>\n",
" <td>0.000526</td>\n",
" <td>0.000019</td>\n",
" <td>7</td>\n",
" <td>multiplicative</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>{'degree': 7, 'deseasonal_model': 'multiplicat...</td>\n",
" <td>0.346732</td>\n",
" <td>0.136618</td>\n",
" <td>0.058932</td>\n",
" <td>0.180761</td>\n",
" <td>0.121569</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.019979</td>\n",
" <td>0.001680</td>\n",
" <td>0.001169</td>\n",
" <td>0.000908</td>\n",
" <td>6</td>\n",
" <td>additive</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>22</td>\n",
" <td>{'degree': 6, 'deseasonal_model': 'additive', ...</td>\n",
" <td>0.465248</td>\n",
" <td>0.122866</td>\n",
" <td>0.169175</td>\n",
" <td>0.252430</td>\n",
" <td>0.151668</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.014420</td>\n",
" <td>0.002230</td>\n",
" <td>0.000435</td>\n",
" <td>0.000092</td>\n",
" <td>9</td>\n",
" <td>multiplicative</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>24</td>\n",
" <td>23</td>\n",
" <td>{'degree': 9, 'deseasonal_model': 'multiplicat...</td>\n",
" <td>0.486684</td>\n",
" <td>0.356662</td>\n",
" <td>0.286260</td>\n",
" <td>0.376535</td>\n",
" <td>0.083021</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.017079</td>\n",
" <td>0.002430</td>\n",
" <td>0.000514</td>\n",
" <td>0.000012</td>\n",
" <td>6</td>\n",
" <td>additive</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>24</td>\n",
" <td>17</td>\n",
" <td>{'degree': 6, 'deseasonal_model': 'additive', ...</td>\n",
" <td>0.878960</td>\n",
" <td>0.395391</td>\n",
" <td>0.332351</td>\n",
" <td>0.535568</td>\n",
" <td>0.244175</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.023635</td>\n",
" <td>0.008199</td>\n",
" <td>0.000577</td>\n",
" <td>0.000073</td>\n",
" <td>8</td>\n",
" <td>additive</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>24</td>\n",
" <td>14</td>\n",
" <td>{'degree': 8, 'deseasonal_model': 'additive', ...</td>\n",
" <td>1.570728</td>\n",
" <td>0.285090</td>\n",
" <td>0.212746</td>\n",
" <td>0.689521</td>\n",
" <td>0.623807</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.018477</td>\n",
" <td>0.001986</td>\n",
" <td>0.000664</td>\n",
" <td>0.000168</td>\n",
" <td>7</td>\n",
" <td>multiplicative</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>24</td>\n",
" <td>13</td>\n",
" <td>{'degree': 7, 'deseasonal_model': 'multiplicat...</td>\n",
" <td>1.186817</td>\n",
" <td>0.547635</td>\n",
" <td>0.345657</td>\n",
" <td>0.693370</td>\n",
" <td>0.358531</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_fit_time std_fit_time ... std_test_smape rank_test_smape\n",
"4 0.018198 0.000647 ... 0.024496 1\n",
"6 0.022529 0.005692 ... 0.030443 2\n",
"7 0.018850 0.001927 ... 0.028150 3\n",
"1 0.020537 0.003949 ... 0.080006 4\n",
"0 0.026006 0.011879 ... 0.121569 5\n",
"5 0.019979 0.001680 ... 0.151668 6\n",
"9 0.014420 0.002230 ... 0.083021 7\n",
"3 0.017079 0.002430 ... 0.244175 8\n",
"2 0.023635 0.008199 ... 0.623807 9\n",
"8 0.018477 0.001986 ... 0.358531 10\n",
"\n",
"[10 rows x 17 columns]"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dKWCVFBdRnwU"
},
"source": [
"Observations\n",
"\n",
"1. Seasonal period of harmonics of 12 (e.g. 24) can be helpful\n",
"2. Lower detrending degree gives better results\n",
"\n",
"Why not limit the search space using these observations to see if we can improve the performance more.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8as2oksS1fnh"
},
"source": [
"## Tuning Customization"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nwZp8OtCeFu0",
"outputId": "98cf736e-b73b-4e95-cccc-5ba96eb69fe4"
},
"source": [
"random_grid['sp'] = [12, 24, 36]\n",
"random_grid['degree'] = [1, 2, 3]\n",
"random_grid"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'degree': [1, 2, 3],\n",
" 'deseasonal_model': CategoricalDistribution(values=['additive', 'multiplicative']),\n",
" 'regressor__fit_intercept': CategoricalDistribution(values=[True, False]),\n",
" 'regressor__normalize': CategoricalDistribution(values=[True, False]),\n",
" 'sp': [12, 24, 36],\n",
" 'window_length': IntUniformDistribution(lower=12, upper=24, log=False)}"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
"2270d7c69841478cb8a605207bd22c61",
"9f1ce398a2244e11b655f1010f43faee",
"7c53fde06be74021935a10e5df8cd1bd"
]
},
"id": "XcQt4eB3eSPG",
"outputId": "75cee6db-99fc-410f-bc15-0abe9e874aea"
},
"source": [
"better_tuned_random = exp.tune_model(model, custom_grid=random_grid, n_iter=20)\n",
"exp.plot_model(better_tuned_random)"
],
"execution_count": 15,
"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>10.1316</td>\n",
" <td>13.9316</td>\n",
" <td>0.0251</td>\n",
" <td>0.0255</td>\n",
" <td>0.9368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12</td>\n",
" <td>28.7971</td>\n",
" <td>32.4905</td>\n",
" <td>0.0803</td>\n",
" <td>0.0763</td>\n",
" <td>0.7235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12</td>\n",
" <td>6.8656</td>\n",
" <td>8.9367</td>\n",
" <td>0.0153</td>\n",
" <td>0.0155</td>\n",
" <td>0.9821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>15.2648</td>\n",
" <td>18.4530</td>\n",
" <td>0.0402</td>\n",
" <td>0.0391</td>\n",
" <td>0.8808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>9.6613</td>\n",
" <td>10.1334</td>\n",
" <td>0.0286</td>\n",
" <td>0.0266</td>\n",
" <td>0.1128</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12 10.1316 13.9316 0.0251 0.0255 0.9368\n",
"1 1957-12 28.7971 32.4905 0.0803 0.0763 0.7235\n",
"2 1958-12 6.8656 8.9367 0.0153 0.0155 0.9821\n",
"Mean NaN 15.2648 18.4530 0.0402 0.0391 0.8808\n",
"SD NaN 9.6613 10.1334 0.0286 0.0266 0.1128"
]
},
"metadata": {}
},
{
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},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "aHNw-PhZ20rC",
"outputId": "16fb4dba-f7f8-4d1a-f424-072dfca1bab4"
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"source": [
"better_tuned_random"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BaseCdsDtForecaster(degree=2, deseasonal_model='multiplicative',\n",
" regressor=LinearRegression(copy_X=True, fit_intercept=False,\n",
" n_jobs=-1, normalize=False,\n",
" positive=False),\n",
" sp=12, window_length=13)"
]
},
"metadata": {},
"execution_count": 16
}
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},
{
"cell_type": "markdown",
"metadata": {
"id": "pouP2picSYuY"
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"source": [
"**Observations**\n",
"\n",
"1. Model performance during cross-validation has been improved. For example\n",
" - MAE reduced from 17.8 in the random grid search case to 15.2 using the custom grid).\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kUlCB5YM7sHu"
},
"source": [
"Happy forecasting!"
]
}
]
}
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