{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deterministic Terms in Time Series Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-07-28T18:48:30.417422Z", "iopub.status.busy": "2026-07-28T18:48:30.416671Z", "iopub.status.idle": "2026-07-28T18:48:31.269800Z", "shell.execute_reply": "2026-07-28T18:48:31.268428Z" } }, "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": "2026-07-28T18:48:31.272522Z", "iopub.status.busy": "2026-07-28T18:48:31.271660Z", "iopub.status.idle": "2026-07-28T18:48:31.542887Z", "shell.execute_reply": "2026-07-28T18:48:31.539511Z" } }, "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": "2026-07-28T18:48:31.544647Z", "iopub.status.busy": "2026-07-28T18:48:31.544441Z", "iopub.status.idle": "2026-07-28T18:48:31.576241Z", "shell.execute_reply": "2026-07-28T18:48:31.575202Z" } }, "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": "2026-07-28T18:48:31.578572Z", "iopub.status.busy": "2026-07-28T18:48:31.577864Z", "iopub.status.idle": "2026-07-28T18:48:31.616338Z", "shell.execute_reply": "2026-07-28T18:48:31.615738Z" } }, "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": "2026-07-28T18:48:31.618588Z", "iopub.status.busy": "2026-07-28T18:48:31.618396Z", "iopub.status.idle": "2026-07-28T18:48:31.646758Z", "shell.execute_reply": "2026-07-28T18:48:31.645732Z" } }, "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": "2026-07-28T18:48:31.648522Z", "iopub.status.busy": "2026-07-28T18:48:31.648327Z", "iopub.status.idle": "2026-07-28T18:48:31.674905Z", "shell.execute_reply": "2026-07-28T18:48:31.671463Z" } }, "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": "2026-07-28T18:48:31.676725Z", "iopub.status.busy": "2026-07-28T18:48:31.676525Z", "iopub.status.idle": "2026-07-28T18:48:31.701355Z", "shell.execute_reply": "2026-07-28T18:48:31.700732Z" } }, "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": "2026-07-28T18:48:31.705468Z", "iopub.status.busy": "2026-07-28T18:48:31.705283Z", "iopub.status.idle": "2026-07-28T18:48:31.729813Z", "shell.execute_reply": "2026-07-28T18:48:31.728732Z" } }, "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": "2026-07-28T18:48:31.732171Z", "iopub.status.busy": "2026-07-28T18:48:31.731442Z", "iopub.status.idle": "2026-07-28T18:48:31.804025Z", "shell.execute_reply": "2026-07-28T18:48:31.803518Z" } }, "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": "2026-07-28T18:48:31.812250Z", "iopub.status.busy": "2026-07-28T18:48:31.812055Z", "iopub.status.idle": "2026-07-28T18:48:31.830686Z", "shell.execute_reply": "2026-07-28T18:48:31.829540Z" } }, "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": "2026-07-28T18:48:31.834850Z", "iopub.status.busy": "2026-07-28T18:48:31.832319Z", "iopub.status.idle": "2026-07-28T18:48:31.871133Z", "shell.execute_reply": "2026-07-28T18:48:31.870592Z" } }, "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": "2026-07-28T18:48:31.875883Z", "iopub.status.busy": "2026-07-28T18:48:31.875675Z", "iopub.status.idle": "2026-07-28T18:48:31.887559Z", "shell.execute_reply": "2026-07-28T18:48:31.886552Z" } }, "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": "2026-07-28T18:48:31.889832Z", "iopub.status.busy": "2026-07-28T18:48:31.889121Z", "iopub.status.idle": "2026-07-28T18:48:31.905844Z", "shell.execute_reply": "2026-07-28T18:48:31.905222Z" } }, "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": [ "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": "2026-07-28T18:48:31.912579Z", "iopub.status.busy": "2026-07-28T18:48:31.912353Z", "iopub.status.idle": "2026-07-28T18:48:31.925055Z", "shell.execute_reply": "2026-07-28T18:48:31.918838Z" } }, "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": "2026-07-28T18:48:31.927194Z", "iopub.status.busy": "2026-07-28T18:48:31.926959Z", "iopub.status.idle": "2026-07-28T18:48:31.947865Z", "shell.execute_reply": "2026-07-28T18:48:31.947170Z" } }, "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": "2026-07-28T18:48:31.956147Z", "iopub.status.busy": "2026-07-28T18:48:31.955954Z", "iopub.status.idle": "2026-07-28T18:48:31.991748Z", "shell.execute_reply": "2026-07-28T18:48:31.979369Z" } }, "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": "2026-07-28T18:48:31.999394Z", "iopub.status.busy": "2026-07-28T18:48:31.999176Z", "iopub.status.idle": "2026-07-28T18:48:32.562204Z", "shell.execute_reply": "2026-07-28T18:48:32.561566Z" } }, "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": "2026-07-28T18:48:32.564785Z", "iopub.status.busy": "2026-07-28T18:48:32.564556Z", "iopub.status.idle": "2026-07-28T18:48:35.135203Z", "shell.execute_reply": "2026-07-28T18:48:35.134631Z" } }, "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: Tue, 28 Jul 2026 AIC 555.927\n", "Time: 18:48:35 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": "2026-07-28T18:48:35.140748Z", "iopub.status.busy": "2026-07-28T18:48:35.140414Z", "iopub.status.idle": "2026-07-28T18:48:35.632122Z", "shell.execute_reply": "2026-07-28T18:48:35.631214Z" } }, "outputs": [ { "data": { "image/png": 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", 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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": "2026-07-28T18:48:35.637483Z", "iopub.status.busy": "2026-07-28T18:48:35.637282Z", "iopub.status.idle": "2026-07-28T18:48:35.659018Z", "shell.execute_reply": "2026-07-28T18:48:35.656071Z" } }, "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": "2026-07-28T18:48:35.664006Z", "iopub.status.busy": "2026-07-28T18:48:35.663768Z", "iopub.status.idle": "2026-07-28T18:48:36.690636Z", "shell.execute_reply": "2026-07-28T18:48:36.689551Z" } }, "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: Tue, 28 Jul 2026 AIC 600.763\n", "Time: 18:48:36 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": "2026-07-28T18:48:36.697312Z", "iopub.status.busy": "2026-07-28T18:48:36.697100Z", "iopub.status.idle": "2026-07-28T18:48:36.722019Z", "shell.execute_reply": "2026-07-28T18:48:36.721527Z" } }, "outputs": [ { "data": { "text/html": [ "
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AutoRegSARIMAX
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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 -17.130963\n", "204 -21.950370 -20.760473\n", "205 -24.790116 -23.475511\n", "206 -26.503171 -25.109629\n", "207 -26.972781 -25.546790\n", "208 -26.141244 -24.728385\n", "209 -24.013773 -22.657094\n", "210 -20.658891 -19.397352\n", "211 -16.205310 -15.072385" ] }, "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 (ipykernel)", "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": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 4 }