{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deterministic Terms in Time Series Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:19.303151Z", "iopub.status.busy": "2022-11-02T17:07:19.302681Z", "iopub.status.idle": "2022-11-02T17:07:20.011655Z", "shell.execute_reply": "2022-11-02T17:07:20.011001Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "plt.rc(\"figure\", figsize=(16, 9))\n", "plt.rc(\"font\", size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Use\n", "\n", "Basic configurations can be directly constructed through `DeterministicProcess`. These can include a constant, a time trend of any order, and either a seasonal or a Fourier component.\n", "\n", "The process requires an index, which is the index of the full-sample (or in-sample).\n", "\n", "First, we initialize a deterministic process with a constant, a linear time trend, and a 5-period seasonal term. The `in_sample` method returns the full set of values that match the index." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.017288Z", "iopub.status.busy": "2022-11-02T17:07:20.016056Z", "iopub.status.idle": "2022-11-02T17:07:20.081674Z", "shell.execute_reply": "2022-11-02T17:07:20.081073Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "0 1.0 1.0 0.0 0.0 0.0 0.0\n", "1 1.0 2.0 1.0 0.0 0.0 0.0\n", "2 1.0 3.0 0.0 1.0 0.0 0.0\n", "3 1.0 4.0 0.0 0.0 1.0 0.0\n", "4 1.0 5.0 0.0 0.0 0.0 1.0\n", ".. ... ... ... ... ... ...\n", "95 1.0 96.0 0.0 0.0 0.0 0.0\n", "96 1.0 97.0 1.0 0.0 0.0 0.0\n", "97 1.0 98.0 0.0 1.0 0.0 0.0\n", "98 1.0 99.0 0.0 0.0 1.0 0.0\n", "99 1.0 100.0 0.0 0.0 0.0 1.0\n", "\n", "[100 rows x 6 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.deterministic import DeterministicProcess\n", "\n", "index = pd.RangeIndex(0, 100)\n", "det_proc = DeterministicProcess(index, constant=True, order=1, seasonal=True, period=5)\n", "det_proc.in_sample()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `out_of_sample` returns the next `steps` values after the end of the in-sample." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.086595Z", "iopub.status.busy": "2022-11-02T17:07:20.085460Z", "iopub.status.idle": "2022-11-02T17:07:20.103212Z", "shell.execute_reply": "2022-11-02T17:07:20.102653Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "100 1.0 101.0 0.0 0.0 0.0 0.0\n", "101 1.0 102.0 1.0 0.0 0.0 0.0\n", "102 1.0 103.0 0.0 1.0 0.0 0.0\n", "103 1.0 104.0 0.0 0.0 1.0 0.0\n", "104 1.0 105.0 0.0 0.0 0.0 1.0\n", "105 1.0 106.0 0.0 0.0 0.0 0.0\n", "106 1.0 107.0 1.0 0.0 0.0 0.0\n", "107 1.0 108.0 0.0 1.0 0.0 0.0\n", "108 1.0 109.0 0.0 0.0 1.0 0.0\n", "109 1.0 110.0 0.0 0.0 0.0 1.0\n", "110 1.0 111.0 0.0 0.0 0.0 0.0\n", "111 1.0 112.0 1.0 0.0 0.0 0.0\n", "112 1.0 113.0 0.0 1.0 0.0 0.0\n", "113 1.0 114.0 0.0 0.0 1.0 0.0\n", "114 1.0 115.0 0.0 0.0 0.0 1.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`range(start, stop)` can also be used to produce the deterministic terms over any range including in- and out-of-sample.\n", "\n", "### Notes\n", "\n", "* When the index is a pandas `DatetimeIndex` or a `PeriodIndex`, then `start` and `stop` can be date-like (strings, e.g., \"2020-06-01\", or Timestamp) or integers.\n", "* `stop` is always included in the range. While this is not very Pythonic, it is needed since both statsmodels and Pandas include `stop` when working with date-like slices." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.107813Z", "iopub.status.busy": "2022-11-02T17:07:20.106721Z", "iopub.status.idle": "2022-11-02T17:07:20.126792Z", "shell.execute_reply": "2022-11-02T17:07:20.126257Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const trend s(2,5) s(3,5) s(4,5) s(5,5)\n", "190 1.0 191.0 0.0 0.0 0.0 0.0\n", "191 1.0 192.0 1.0 0.0 0.0 0.0\n", "192 1.0 193.0 0.0 1.0 0.0 0.0\n", "193 1.0 194.0 0.0 0.0 1.0 0.0\n", "194 1.0 195.0 0.0 0.0 0.0 1.0\n", "195 1.0 196.0 0.0 0.0 0.0 0.0\n", "196 1.0 197.0 1.0 0.0 0.0 0.0\n", "197 1.0 198.0 0.0 1.0 0.0 0.0\n", "198 1.0 199.0 0.0 0.0 1.0 0.0\n", "199 1.0 200.0 0.0 0.0 0.0 1.0\n", "200 1.0 201.0 0.0 0.0 0.0 0.0\n", "201 1.0 202.0 1.0 0.0 0.0 0.0\n", "202 1.0 203.0 0.0 1.0 0.0 0.0\n", "203 1.0 204.0 0.0 0.0 1.0 0.0\n", "204 1.0 205.0 0.0 0.0 0.0 1.0\n", "205 1.0 206.0 0.0 0.0 0.0 0.0\n", "206 1.0 207.0 1.0 0.0 0.0 0.0\n", "207 1.0 208.0 0.0 1.0 0.0 0.0\n", "208 1.0 209.0 0.0 0.0 1.0 0.0\n", "209 1.0 210.0 0.0 0.0 0.0 1.0\n", "210 1.0 211.0 0.0 0.0 0.0 0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(190, 210)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a Date-like Index\n", "\n", "Next, we show the same steps using a `PeriodIndex`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.131386Z", "iopub.status.busy": "2022-11-02T17:07:20.130295Z", "iopub.status.idle": "2022-11-02T17:07:20.147175Z", "shell.execute_reply": "2022-11-02T17:07:20.146629Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2020-03 1.0 0.000000e+00 1.000000e+00 0.000000e+00 1.0\n", "2020-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2020-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2020-06 1.0 1.000000e+00 6.123234e-17 1.224647e-16 -1.0\n", "2020-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2020-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2020-09 1.0 1.224647e-16 -1.000000e+00 -2.449294e-16 1.0\n", "2020-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2020-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2020-12 1.0 -1.000000e+00 -1.836970e-16 3.673940e-16 -1.0\n", "2021-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2021-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index = pd.period_range(\"2020-03-01\", freq=\"M\", periods=60)\n", "det_proc = DeterministicProcess(index, constant=True, fourier=2)\n", "det_proc.in_sample().head(12)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.151666Z", "iopub.status.busy": "2022-11-02T17:07:20.150589Z", "iopub.status.idle": "2022-11-02T17:07:20.165633Z", "shell.execute_reply": "2022-11-02T17:07:20.165112Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2026-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`range` accepts date-like arguments, which are usually given as strings." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.170188Z", "iopub.status.busy": "2022-11-02T17:07:20.169036Z", "iopub.status.idle": "2022-11-02T17:07:20.184000Z", "shell.execute_reply": "2022-11-02T17:07:20.183470Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2025-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(\"2025-01\", \"2026-01\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is equivalent to using the integer values 58 and 70." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.188401Z", "iopub.status.busy": "2022-11-02T17:07:20.187328Z", "iopub.status.idle": "2022-11-02T17:07:20.202179Z", "shell.execute_reply": "2022-11-02T17:07:20.201638Z" } }, "outputs": [ { "data": { "text/html": [ "
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constsin(1,12)cos(1,12)sin(2,12)cos(2,12)
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" ], "text/plain": [ " const sin(1,12) cos(1,12) sin(2,12) cos(2,12)\n", "2025-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5\n", "2025-02 1.0 -5.000000e-01 8.660254e-01 -8.660254e-01 0.5\n", "2025-03 1.0 -1.224647e-15 1.000000e+00 -2.449294e-15 1.0\n", "2025-04 1.0 5.000000e-01 8.660254e-01 8.660254e-01 0.5\n", "2025-05 1.0 8.660254e-01 5.000000e-01 8.660254e-01 -0.5\n", "2025-06 1.0 1.000000e+00 -4.904777e-16 -9.809554e-16 -1.0\n", "2025-07 1.0 8.660254e-01 -5.000000e-01 -8.660254e-01 -0.5\n", "2025-08 1.0 5.000000e-01 -8.660254e-01 -8.660254e-01 0.5\n", "2025-09 1.0 4.899825e-15 -1.000000e+00 -9.799650e-15 1.0\n", "2025-10 1.0 -5.000000e-01 -8.660254e-01 8.660254e-01 0.5\n", "2025-11 1.0 -8.660254e-01 -5.000000e-01 8.660254e-01 -0.5\n", "2025-12 1.0 -1.000000e+00 -3.184701e-15 6.369401e-15 -1.0\n", "2026-01 1.0 -8.660254e-01 5.000000e-01 -8.660254e-01 -0.5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.range(58, 70)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Construction\n", "\n", "Deterministic processes with features not supported directly through the constructor can be created using `additional_terms` which accepts a list of `DetermisticTerm`. Here we create a deterministic process with two seasonal components: day-of-week with a 5 day period and an annual captured through a Fourier component with a period of 365.25 days." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.206545Z", "iopub.status.busy": "2022-11-02T17:07:20.205431Z", "iopub.status.idle": "2022-11-02T17:07:20.237663Z", "shell.execute_reply": "2022-11-02T17:07:20.237133Z" } }, "outputs": [ { "data": { "text/html": [ "
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consts(2,7)s(3,7)s(4,7)s(5,7)s(6,7)s(7,7)sin(1,365.25)cos(1,365.25)sin(2,365.25)cos(2,365.25)
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2020-03-281.00.00.00.00.00.01.00.4479450.8940610.8009800.598691
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" ], "text/plain": [ " const s(2,7) s(3,7) s(4,7) s(5,7) s(6,7) s(7,7) \\\n", "2020-03-01 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-02 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-03 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-04 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-05 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-06 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-07 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-08 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-09 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-10 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-11 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-12 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-13 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-14 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-15 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-16 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-17 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-18 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-19 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-20 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-21 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2020-03-22 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-23 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n", "2020-03-24 1.0 0.0 1.0 0.0 0.0 0.0 0.0 \n", "2020-03-25 1.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", "2020-03-26 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "2020-03-27 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", "2020-03-28 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "\n", " sin(1,365.25) cos(1,365.25) sin(2,365.25) cos(2,365.25) \n", "2020-03-01 0.000000 1.000000 0.000000 1.000000 \n", "2020-03-02 0.017202 0.999852 0.034398 0.999408 \n", "2020-03-03 0.034398 0.999408 0.068755 0.997634 \n", "2020-03-04 0.051584 0.998669 0.103031 0.994678 \n", "2020-03-05 0.068755 0.997634 0.137185 0.990545 \n", "2020-03-06 0.085906 0.996303 0.171177 0.985240 \n", "2020-03-07 0.103031 0.994678 0.204966 0.978769 \n", "2020-03-08 0.120126 0.992759 0.238513 0.971139 \n", "2020-03-09 0.137185 0.990545 0.271777 0.962360 \n", "2020-03-10 0.154204 0.988039 0.304719 0.952442 \n", "2020-03-11 0.171177 0.985240 0.337301 0.941397 \n", "2020-03-12 0.188099 0.982150 0.369484 0.929237 \n", "2020-03-13 0.204966 0.978769 0.401229 0.915978 \n", "2020-03-14 0.221772 0.975099 0.432499 0.901634 \n", "2020-03-15 0.238513 0.971139 0.463258 0.886224 \n", "2020-03-16 0.255182 0.966893 0.493468 0.869764 \n", "2020-03-17 0.271777 0.962360 0.523094 0.852275 \n", "2020-03-18 0.288291 0.957543 0.552101 0.833777 \n", "2020-03-19 0.304719 0.952442 0.580455 0.814292 \n", "2020-03-20 0.321058 0.947060 0.608121 0.793844 \n", "2020-03-21 0.337301 0.941397 0.635068 0.772456 \n", "2020-03-22 0.353445 0.935455 0.661263 0.750154 \n", "2020-03-23 0.369484 0.929237 0.686676 0.726964 \n", "2020-03-24 0.385413 0.922744 0.711276 0.702913 \n", "2020-03-25 0.401229 0.915978 0.735034 0.678031 \n", "2020-03-26 0.416926 0.908940 0.757922 0.652346 \n", "2020-03-27 0.432499 0.901634 0.779913 0.625889 \n", "2020-03-28 0.447945 0.894061 0.800980 0.598691 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.tsa.deterministic import Fourier, Seasonality, TimeTrend\n", "\n", "index = pd.period_range(\"2020-03-01\", freq=\"D\", periods=2 * 365)\n", "tt = TimeTrend(constant=True)\n", "four = Fourier(period=365.25, order=2)\n", "seas = Seasonality(period=7)\n", "det_proc = DeterministicProcess(index, additional_terms=[tt, seas, four])\n", "det_proc.in_sample().head(28)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Deterministic Terms\n", "\n", "The `DetermisticTerm` Abstract Base Class is designed to be subclassed to help users write custom deterministic terms. We next show two examples. The first is a broken time trend that allows a break after a fixed number of periods. The second is a \"trick\" deterministic term that allows exogenous data, which is not really a deterministic process, to be treated as if was deterministic. This lets use simplify gathering the terms needed for forecasting.\n", "\n", "These are intended to demonstrate the construction of custom terms. They can definitely be improved in terms of input validation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.242123Z", "iopub.status.busy": "2022-11-02T17:07:20.241027Z", "iopub.status.idle": "2022-11-02T17:07:20.248942Z", "shell.execute_reply": "2022-11-02T17:07:20.248421Z" } }, "outputs": [], "source": [ "from statsmodels.tsa.deterministic import DeterministicTerm\n", "\n", "\n", "class BrokenTimeTrend(DeterministicTerm):\n", " def __init__(self, break_period: int):\n", " self._break_period = break_period\n", "\n", " def __str__(self):\n", " return \"Broken Time Trend\"\n", "\n", " def _eq_attr(self):\n", " return (self._break_period,)\n", "\n", " def in_sample(self, index: pd.Index):\n", " nobs = index.shape[0]\n", " terms = np.zeros((nobs, 2))\n", " terms[self._break_period :, 0] = 1\n", " terms[self._break_period :, 1] = np.arange(self._break_period + 1, nobs + 1)\n", " return pd.DataFrame(terms, columns=[\"const_break\", \"trend_break\"], index=index)\n", "\n", " def out_of_sample(\n", " self, steps: int, index: pd.Index, forecast_index: pd.Index = None\n", " ):\n", " # Always call extend index first\n", " fcast_index = self._extend_index(index, steps, forecast_index)\n", " nobs = index.shape[0]\n", " terms = np.zeros((steps, 2))\n", " # Assume break period is in-sample\n", " terms[:, 0] = 1\n", " terms[:, 1] = np.arange(nobs + 1, nobs + steps + 1)\n", " return pd.DataFrame(\n", " terms, columns=[\"const_break\", \"trend_break\"], index=fcast_index\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.253210Z", "iopub.status.busy": "2022-11-02T17:07:20.252132Z", "iopub.status.idle": "2022-11-02T17:07:20.267855Z", "shell.execute_reply": "2022-11-02T17:07:20.267334Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendconst_breaktrend_break
551.056.00.00.0
561.057.00.00.0
571.058.00.00.0
581.059.00.00.0
591.060.00.00.0
601.061.01.061.0
611.062.01.062.0
621.063.01.063.0
631.064.01.064.0
641.065.01.065.0
651.066.01.066.0
\n", "
" ], "text/plain": [ " const trend const_break trend_break\n", "55 1.0 56.0 0.0 0.0\n", "56 1.0 57.0 0.0 0.0\n", "57 1.0 58.0 0.0 0.0\n", "58 1.0 59.0 0.0 0.0\n", "59 1.0 60.0 0.0 0.0\n", "60 1.0 61.0 1.0 61.0\n", "61 1.0 62.0 1.0 62.0\n", "62 1.0 63.0 1.0 63.0\n", "63 1.0 64.0 1.0 64.0\n", "64 1.0 65.0 1.0 65.0\n", "65 1.0 66.0 1.0 66.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "btt = BrokenTimeTrend(60)\n", "tt = TimeTrend(constant=True, order=1)\n", "index = pd.RangeIndex(100)\n", "det_proc = DeterministicProcess(index, additional_terms=[tt, btt])\n", "det_proc.range(55, 65)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we write a simple \"wrapper\" for some actual exogenous data that simplifies constructing out-of-sample exogenous arrays for forecasting." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.272214Z", "iopub.status.busy": "2022-11-02T17:07:20.271145Z", "iopub.status.idle": "2022-11-02T17:07:20.276982Z", "shell.execute_reply": "2022-11-02T17:07:20.276471Z" } }, "outputs": [], "source": [ "class ExogenousProcess(DeterministicTerm):\n", " def __init__(self, data):\n", " self._data = data\n", "\n", " def __str__(self):\n", " return \"Custom Exog Process\"\n", "\n", " def _eq_attr(self):\n", " return (id(self._data),)\n", "\n", " def in_sample(self, index: pd.Index):\n", " return self._data.loc[index]\n", "\n", " def out_of_sample(\n", " self, steps: int, index: pd.Index, forecast_index: pd.Index = None\n", " ):\n", " forecast_index = self._extend_index(index, steps, forecast_index)\n", " return self._data.loc[forecast_index]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.281179Z", "iopub.status.busy": "2022-11-02T17:07:20.280104Z", "iopub.status.idle": "2022-11-02T17:07:20.289324Z", "shell.execute_reply": "2022-11-02T17:07:20.288807Z" } }, "outputs": [ { "data": { "text/html": [ "
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exog1exog2
0699
16428
21581
3548
4128
\n", "
" ], "text/plain": [ " exog1 exog2\n", "0 6 99\n", "1 64 28\n", "2 15 81\n", "3 54 8\n", "4 12 8" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "gen = np.random.default_rng(98765432101234567890)\n", "exog = pd.DataFrame(gen.integers(100, size=(300, 2)), columns=[\"exog1\", \"exog2\"])\n", "exog.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.294205Z", "iopub.status.busy": "2022-11-02T17:07:20.293094Z", "iopub.status.idle": "2022-11-02T17:07:20.297867Z", "shell.execute_reply": "2022-11-02T17:07:20.297355Z" } }, "outputs": [], "source": [ "ep = ExogenousProcess(exog)\n", "tt = TimeTrend(constant=True, order=1)\n", "# The in-sample index\n", "idx = exog.index[:200]\n", "det_proc = DeterministicProcess(idx, additional_terms=[tt, ep])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.302148Z", "iopub.status.busy": "2022-11-02T17:07:20.301057Z", "iopub.status.idle": "2022-11-02T17:07:20.313851Z", "shell.execute_reply": "2022-11-02T17:07:20.313328Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendexog1exog2
01.01.0699
11.02.06428
21.03.01581
31.04.0548
41.05.0128
\n", "
" ], "text/plain": [ " const trend exog1 exog2\n", "0 1.0 1.0 6 99\n", "1 1.0 2.0 64 28\n", "2 1.0 3.0 15 81\n", "3 1.0 4.0 54 8\n", "4 1.0 5.0 12 8" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.in_sample().head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.318093Z", "iopub.status.busy": "2022-11-02T17:07:20.317005Z", "iopub.status.idle": "2022-11-02T17:07:20.328481Z", "shell.execute_reply": "2022-11-02T17:07:20.327955Z" } }, "outputs": [ { "data": { "text/html": [ "
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consttrendexog1exog2
2001.0201.05688
2011.0202.04884
2021.0203.0445
2031.0204.06563
2041.0205.06339
2051.0206.08939
2061.0207.04154
2071.0208.0715
2081.0209.0896
2091.0210.05863
\n", "
" ], "text/plain": [ " const trend exog1 exog2\n", "200 1.0 201.0 56 88\n", "201 1.0 202.0 48 84\n", "202 1.0 203.0 44 5\n", "203 1.0 204.0 65 63\n", "204 1.0 205.0 63 39\n", "205 1.0 206.0 89 39\n", "206 1.0 207.0 41 54\n", "207 1.0 208.0 71 5\n", "208 1.0 209.0 89 6\n", "209 1.0 210.0 58 63" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_proc.out_of_sample(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Support\n", "\n", "The only model that directly supports `DeterministicProcess` is `AutoReg`. A custom term can be set using the `deterministic` keyword argument. \n", "\n", "**Note**: Using a custom term requires that `trend=\"n\"` and `seasonal=False` so that all deterministic components must come from the custom deterministic term." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate Some Data\n", "\n", "Here we simulate some data that has an weekly seasonality captured by a Fourier series." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.333313Z", "iopub.status.busy": "2022-11-02T17:07:20.332236Z", "iopub.status.idle": "2022-11-02T17:07:20.593913Z", "shell.execute_reply": "2022-11-02T17:07:20.593290Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gen = np.random.default_rng(98765432101234567890)\n", "idx = pd.RangeIndex(200)\n", "det_proc = DeterministicProcess(idx, constant=True, period=52, fourier=2)\n", "det_terms = det_proc.in_sample().to_numpy()\n", "params = np.array([1.0, 3, -1, 4, -2])\n", "exog = det_terms @ params\n", "y = np.empty(200)\n", "y[0] = det_terms[0] @ params + gen.standard_normal()\n", "for i in range(1, 200):\n", " y[i] = 0.9 * y[i - 1] + det_terms[i] @ params + gen.standard_normal()\n", "y = pd.Series(y, index=idx)\n", "ax = y.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is then fit using the `deterministic` keyword argument. `seasonal` defaults to False but `trend` defaults to `\"c\"` so this needs to be changed." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.597972Z", "iopub.status.busy": "2022-11-02T17:07:20.597700Z", "iopub.status.idle": "2022-11-02T17:07:20.966490Z", "shell.execute_reply": "2022-11-02T17:07:20.965388Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AutoReg Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 200\n", "Model: AutoReg(1) Log Likelihood -270.964\n", "Method: Conditional MLE S.D. of innovations 0.944\n", "Date: Wed, 02 Nov 2022 AIC 555.927\n", "Time: 17:07:20 BIC 578.980\n", "Sample: 1 HQIC 565.258\n", " 200 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.8436 0.172 4.916 0.000 0.507 1.180\n", "sin(1,52) 2.9738 0.160 18.587 0.000 2.660 3.287\n", "cos(1,52) -0.6771 0.284 -2.380 0.017 -1.235 -0.120\n", "sin(2,52) 3.9951 0.099 40.336 0.000 3.801 4.189\n", "cos(2,52) -1.7206 0.264 -6.519 0.000 -2.238 -1.203\n", "y.L1 0.9116 0.014 63.264 0.000 0.883 0.940\n", " Roots \n", "=============================================================================\n", " Real Imaginary Modulus Frequency\n", "-----------------------------------------------------------------------------\n", "AR.1 1.0970 +0.0000j 1.0970 0.0000\n", "-----------------------------------------------------------------------------\n" ] } ], "source": [ "from statsmodels.tsa.api import AutoReg\n", "\n", "mod = AutoReg(y, 1, trend=\"n\", deterministic=det_proc)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `plot_predict` to show the predicted values and their prediction interval. The out-of-sample deterministic values are automatically produced by the deterministic process passed to `AutoReg`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:20.970001Z", "iopub.status.busy": "2022-11-02T17:07:20.969731Z", "iopub.status.idle": "2022-11-02T17:07:21.183256Z", "shell.execute_reply": "2022-11-02T17:07:21.182556Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = res.plot_predict(200, 200 + 2 * 52, True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:21.187149Z", "iopub.status.busy": "2022-11-02T17:07:21.186514Z", "iopub.status.idle": "2022-11-02T17:07:21.197821Z", "shell.execute_reply": "2022-11-02T17:07:21.196265Z" } }, "outputs": [ { "data": { "text/plain": [ "200 -3.253482\n", "201 -8.555660\n", "202 -13.607557\n", "203 -18.152622\n", "204 -21.950370\n", "205 -24.790116\n", "206 -26.503171\n", "207 -26.972781\n", "208 -26.141244\n", "209 -24.013773\n", "210 -20.658891\n", "211 -16.205310\n", "dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auto_reg_forecast = res.predict(200, 211)\n", "auto_reg_forecast" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using with other models\n", "\n", "Other models do not support `DeterministicProcess` directly. We can instead manually pass any deterministic terms as `exog` to model that support exogenous values.\n", "\n", "Note that `SARIMAX` with exogenous variables is OLS with SARIMA errors so that the model is \n", "\n", "$$\n", "\\begin{align*}\n", "\\nu_t & = y_t - x_t \\beta \\\\\n", "(1-\\phi(L))\\nu_t & = (1+\\theta(L))\\epsilon_t.\n", "\\end{align*}\n", "$$\n", "\n", "The parameters on deterministic terms are not directly comparable to `AutoReg` which evolves according to the equation\n", "\n", "$$\n", "(1-\\phi(L)) y_t = x_t \\beta + \\epsilon_t.\n", "$$\n", "\n", "When $x_t$ contains only deterministic terms, these two representation are equivalent (assuming $\\theta(L)=0$ so that there is no MA).\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:21.201348Z", "iopub.status.busy": "2022-11-02T17:07:21.200965Z", "iopub.status.idle": "2022-11-02T17:07:21.419312Z", "shell.execute_reply": "2022-11-02T17:07:21.417839Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 200\n", "Model: SARIMAX(1, 0, 0) Log Likelihood -293.381\n", "Date: Wed, 02 Nov 2022 AIC 600.763\n", "Time: 17:07:21 BIC 623.851\n", "Sample: 0 HQIC 610.106\n", " - 200 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "intercept 0.0797 0.140 0.567 0.570 -0.196 0.355\n", "sin(1,52) 9.1916 0.876 10.492 0.000 7.475 10.909\n", "cos(1,52) -17.4348 0.891 -19.576 0.000 -19.180 -15.689\n", "sin(2,52) 1.2512 0.466 2.683 0.007 0.337 2.165\n", "cos(2,52) -17.1863 0.434 -39.583 0.000 -18.037 -16.335\n", "ar.L1 0.9957 0.007 150.762 0.000 0.983 1.009\n", "sigma2 1.0748 0.119 9.068 0.000 0.842 1.307\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 2.16 Jarque-Bera (JB): 1.03\n", "Prob(Q): 0.14 Prob(JB): 0.60\n", "Heteroskedasticity (H): 0.71 Skew: -0.14\n", "Prob(H) (two-sided): 0.16 Kurtosis: 2.78\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "from statsmodels.tsa.api import SARIMAX\n", "\n", "det_proc = DeterministicProcess(idx, period=52, fourier=2)\n", "det_terms = det_proc.in_sample()\n", "\n", "mod = SARIMAX(y, order=(1, 0, 0), trend=\"c\", exog=det_terms)\n", "res = mod.fit(disp=False)\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The forecasts are similar but differ since the parameters of the `SARIMAX` are estimated using MLE while `AutoReg` uses OLS." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:07:21.423132Z", "iopub.status.busy": "2022-11-02T17:07:21.422886Z", "iopub.status.idle": "2022-11-02T17:07:21.437404Z", "shell.execute_reply": "2022-11-02T17:07:21.436581Z" } }, "outputs": [ { "data": { "text/html": [ "
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AutoRegSARIMAX
200-3.253482-2.956562
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202-13.607557-12.794072
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" ], "text/plain": [ " AutoReg SARIMAX\n", "200 -3.253482 -2.956562\n", "201 -8.555660 -7.985588\n", "202 -13.607557 -12.794072\n", "203 -18.152622 -17.130962\n", "204 -21.950370 -20.760472\n", "205 -24.790116 -23.475510\n", "206 -26.503171 -25.109628\n", "207 -26.972781 -25.546788\n", "208 -26.141244 -24.728384\n", "209 -24.013773 -22.657093\n", "210 -20.658891 -19.397351\n", "211 -16.205310 -15.072384" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sarimax_forecast = res.forecast(12, exog=det_proc.out_of_sample(12))\n", "df = pd.concat([auto_reg_forecast, sarimax_forecast], axis=1)\n", "df.columns = columns = [\"AutoReg\", \"SARIMAX\"]\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 }