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@ngupta23
Created March 31, 2022 16:53
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pmdarima_vs_statsforecast.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "pmdarima_vs_statsforecast.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPBfdZVGoht6nwvLCP5dvn+",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ngupta23/59cc0ce155048f72b80a0431c57b7d17/pmdarima_vs_statsforecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lu0LObqkp5in",
"outputId": "fb6ac3f2-1dbc-4c08-f040-4ba5e5eb46e8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"System:\n",
" python: 3.7.13 (default, Mar 16 2022, 17:37:17) [GCC 7.5.0]\n",
"executable: /usr/bin/python3\n",
" machine: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n",
"\n",
"Python dependencies:\n",
" pip: 21.1.3\n",
" setuptools: 57.4.0\n",
" sklearn: 1.0.2\n",
" sktime: 0.11.0\n",
" statsmodels: 0.12.2\n",
" numpy: 1.21.5\n",
" scipy: 1.7.3\n",
" pandas: 1.3.5\n",
" matplotlib: 3.2.2\n",
" joblib: 1.0.1\n",
" numba: 0.55.1\n",
" pmdarima: 1.8.5\n",
" tsfresh: None\n"
]
}
],
"source": [
"#### Install and Load Libraries ----\n",
"\n",
"try:\n",
" from sktime import show_versions\n",
"except ModuleNotFoundError:\n",
" !pip install sktime==0.11.0\n",
"\n",
"try:\n",
" import statsforecast\n",
"except ModuleNotFoundError:\n",
" !pip install statsforecast\n",
"\n",
"from sktime import show_versions\n",
"show_versions()\n",
"\n",
"import numpy as np\n",
"import time\n",
"from sktime.forecasting.arima import AutoARIMA\n",
"from sktime.forecasting.statsforecast import StatsForecastAutoARIMA\n",
"from sktime.forecasting.model_selection import temporal_train_test_split\n",
"from sktime.utils.plotting import plot_series"
]
},
{
"cell_type": "code",
"source": [
"#### Helper Functions ----\n",
"import functools\n",
"import time\n",
"\n",
"def timer(func):\n",
" \"\"\"Time how long a function takes to execute.\n",
"\n",
" Parameters\n",
" ----------\n",
" func : function\n",
" The function to time.\n",
" \"\"\"\n",
"\n",
" @functools.wraps(func)\n",
" def wrapper_timer(*args, **kwargs):\n",
" start_time = time.perf_counter()\n",
" value = func(*args, **kwargs)\n",
" elapsed_time = time.perf_counter() - start_time\n",
" print(f\"Finished {func.__name__!r} in {elapsed_time:.4f} seconds\")\n",
" return value\n",
"\n",
" return wrapper_timer\n",
"\n",
"@timer\n",
"def train(model, data):\n",
" model.fit(data)"
],
"metadata": {
"id": "_rZPYYTKxxfx"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#### Load the data ----\n",
"from sktime.datasets import load_airline\n",
"data = load_airline()\n",
"data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "R8QrFnBXqJsb",
"outputId": "f163b41e-4967-42d0-9f4e-327b0fa2c6cd"
},
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"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": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"#### Set Forecasting Horizon ----\n",
"fh = np.arange(1, 13)\n",
"fh"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WRl1m8lB1G8g",
"outputId": "bb01fdb1-f93e-4b0f-f7dd-ab4f68cc8841"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"## Train & Evaluate models"
],
"metadata": {
"id": "4sRxfNOd3t9Z"
}
},
{
"cell_type": "code",
"source": [
"y_train, y_test = temporal_train_test_split(data, fh=fh)\n",
"y_train.shape, y_test.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZL_apdMK0e3G",
"outputId": "8c2a2446-e4b6-4352-e2c4-2882c0944e7b"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((132,), (12,))"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"#### Train pmdarima AutoARIMA model ----\n",
"slow = AutoARIMA(sp=12, supress_warnings=True)\n",
"train(model=slow, data=y_train)\n",
"y_pred_slow = slow.predict(fh)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "unQNP9RTrihx",
"outputId": "4c72b80f-57ed-422e-83f5-114261be20dd"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" ConvergenceWarning)\n",
"/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" ConvergenceWarning)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Finished 'train' in 34.1304 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#### Train statsforecast AutoARIMA model ----\n",
"fast = StatsForecastAutoARIMA(sp=12)\n",
"train(model=fast, data=y_train)\n",
"y_pred_fast = fast.predict(fh)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "11XYken3rpty",
"outputId": "a5bc4ab1-d398-40db-b17d-7f5b91055f5e"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Finished 'train' in 15.0740 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"_ = plot_series(data, y_pred_slow, y_pred_fast, labels=[\"data\", \"pmdarima\", \"statsforecast\"])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "djUUV0JNti_7",
"outputId": "c1d28b76-30fb-4596-c17d-a376fd9cd0de"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"**Observations:**\n",
"* The performance on the test set is almost identical, but ...\n",
"* The training time for statsforecast is almost half."
],
"metadata": {
"id": "8vPruNPw4EAk"
}
},
{
"cell_type": "markdown",
"source": [
"## Refit on entire dataset\n",
"\n",
"Make true future forecasts"
],
"metadata": {
"id": "SWnGFWEd3TNR"
}
},
{
"cell_type": "code",
"source": [
"#### Retrain on entire dataset (pmdarima) ----\n",
"slow = AutoARIMA(sp=12, supress_warnings=True)\n",
"train(model=slow, data=data)\n",
"y_pred_slow = slow.predict(fh)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uoRWpMaZ18I9",
"outputId": "abc75f91-b724-4d48-8939-46f3f9b9caf9"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" ConvergenceWarning)\n",
"/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" ConvergenceWarning)\n",
"/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" ConvergenceWarning)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Finished 'train' in 28.6062 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#### Retrain on entire dataset (statsforecast) ----\n",
"fast = StatsForecastAutoARIMA(sp=12)\n",
"train(model=fast, data=data)\n",
"y_pred_fast = fast.predict(fh)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2k7kGT5s3XrA",
"outputId": "7fe2bbb8-2e5e-4064-9fc8-4f9f7dbe857f"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Finished 'train' in 1.3486 seconds\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"_ = plot_series(data, y_pred_slow, y_pred_fast, labels=[\"data\", \"pmdarima\", \"statsforecast\"])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "CAokZUhQ3db4",
"outputId": "9b981965-bcaf-4e1a-95b0-3d75e97d673b"
},
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"**Observations:**\n",
"* The future predictions are almost identical, but ...\n",
"* The retraining time for statsforecast is 10x faster."
],
"metadata": {
"id": "UiYbRNhE4zZY"
}
}
]
}
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