{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deterministic Terms in Time Series Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>trend</th>\n",
       "      <th>s(2,5)</th>\n",
       "      <th>s(3,5)</th>\n",
       "      <th>s(4,5)</th>\n",
       "      <th>s(5,5)</th>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
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       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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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": {
 
 
 
 
    }
   },
   "outputs": [
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>200</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>1.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>sin(1,12)</th>\n",
       "      <th>cos(1,12)</th>\n",
       "      <th>sin(2,12)</th>\n",
       "      <th>cos(2,12)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-2.449294e-16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.836970e-16</td>\n",
       "      <td>3.673940e-16</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>sin(1,12)</th>\n",
       "      <th>cos(1,12)</th>\n",
       "      <th>sin(2,12)</th>\n",
       "      <th>cos(2,12)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.224647e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.449294e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-4.904777e-16</td>\n",
       "      <td>-9.809554e-16</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.899825e-15</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-9.799650e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-3.184701e-15</td>\n",
       "      <td>6.369401e-15</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>sin(1,12)</th>\n",
       "      <th>cos(1,12)</th>\n",
       "      <th>sin(2,12)</th>\n",
       "      <th>cos(2,12)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
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       "    <tr>\n",
       "      <th>2025-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.224647e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.449294e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-4.904777e-16</td>\n",
       "      <td>-9.809554e-16</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.899825e-15</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-9.799650e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-3.184701e-15</td>\n",
       "      <td>6.369401e-15</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>sin(1,12)</th>\n",
       "      <th>cos(1,12)</th>\n",
       "      <th>sin(2,12)</th>\n",
       "      <th>cos(2,12)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.224647e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.449294e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-4.904777e-16</td>\n",
       "      <td>-9.809554e-16</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.899825e-15</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-9.799650e-15</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-3.184701e-15</td>\n",
       "      <td>6.369401e-15</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>s(2,7)</th>\n",
       "      <th>s(3,7)</th>\n",
       "      <th>s(4,7)</th>\n",
       "      <th>s(5,7)</th>\n",
       "      <th>s(6,7)</th>\n",
       "      <th>s(7,7)</th>\n",
       "      <th>sin(1,365.25)</th>\n",
       "      <th>cos(1,365.25)</th>\n",
       "      <th>sin(2,365.25)</th>\n",
       "      <th>cos(2,365.25)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017202</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>0.034398</td>\n",
       "      <td>0.999408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034398</td>\n",
       "      <td>0.999408</td>\n",
       "      <td>0.068755</td>\n",
       "      <td>0.997634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051584</td>\n",
       "      <td>0.998669</td>\n",
       "      <td>0.103031</td>\n",
       "      <td>0.994678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.068755</td>\n",
       "      <td>0.997634</td>\n",
       "      <td>0.137185</td>\n",
       "      <td>0.990545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085906</td>\n",
       "      <td>0.996303</td>\n",
       "      <td>0.171177</td>\n",
       "      <td>0.985240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.103031</td>\n",
       "      <td>0.994678</td>\n",
       "      <td>0.204966</td>\n",
       "      <td>0.978769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120126</td>\n",
       "      <td>0.992759</td>\n",
       "      <td>0.238513</td>\n",
       "      <td>0.971139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137185</td>\n",
       "      <td>0.990545</td>\n",
       "      <td>0.271777</td>\n",
       "      <td>0.962360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.154204</td>\n",
       "      <td>0.988039</td>\n",
       "      <td>0.304719</td>\n",
       "      <td>0.952442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171177</td>\n",
       "      <td>0.985240</td>\n",
       "      <td>0.337301</td>\n",
       "      <td>0.941397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188099</td>\n",
       "      <td>0.982150</td>\n",
       "      <td>0.369484</td>\n",
       "      <td>0.929237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.204966</td>\n",
       "      <td>0.978769</td>\n",
       "      <td>0.401229</td>\n",
       "      <td>0.915978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.221772</td>\n",
       "      <td>0.975099</td>\n",
       "      <td>0.432499</td>\n",
       "      <td>0.901634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.238513</td>\n",
       "      <td>0.971139</td>\n",
       "      <td>0.463258</td>\n",
       "      <td>0.886224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.255182</td>\n",
       "      <td>0.966893</td>\n",
       "      <td>0.493468</td>\n",
       "      <td>0.869764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.271777</td>\n",
       "      <td>0.962360</td>\n",
       "      <td>0.523094</td>\n",
       "      <td>0.852275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.288291</td>\n",
       "      <td>0.957543</td>\n",
       "      <td>0.552101</td>\n",
       "      <td>0.833777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.304719</td>\n",
       "      <td>0.952442</td>\n",
       "      <td>0.580455</td>\n",
       "      <td>0.814292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321058</td>\n",
       "      <td>0.947060</td>\n",
       "      <td>0.608121</td>\n",
       "      <td>0.793844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.337301</td>\n",
       "      <td>0.941397</td>\n",
       "      <td>0.635068</td>\n",
       "      <td>0.772456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353445</td>\n",
       "      <td>0.935455</td>\n",
       "      <td>0.661263</td>\n",
       "      <td>0.750154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.369484</td>\n",
       "      <td>0.929237</td>\n",
       "      <td>0.686676</td>\n",
       "      <td>0.726964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.385413</td>\n",
       "      <td>0.922744</td>\n",
       "      <td>0.711276</td>\n",
       "      <td>0.702913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.401229</td>\n",
       "      <td>0.915978</td>\n",
       "      <td>0.735034</td>\n",
       "      <td>0.678031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416926</td>\n",
       "      <td>0.908940</td>\n",
       "      <td>0.757922</td>\n",
       "      <td>0.652346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.432499</td>\n",
       "      <td>0.901634</td>\n",
       "      <td>0.779913</td>\n",
       "      <td>0.625889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-28</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.894061</td>\n",
       "      <td>0.800980</td>\n",
       "      <td>0.598691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "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>const</th>\n",
       "      <th>trend</th>\n",
       "      <th>const_break</th>\n",
       "      <th>trend_break</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exog1</th>\n",
       "      <th>exog2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>const</th>\n",
       "      <th>trend</th>\n",
       "      <th>exog1</th>\n",
       "      <th>exog2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>const</th>\n",
       "      <th>trend</th>\n",
       "      <th>exog1</th>\n",
       "      <th>exog2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>56</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>48</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>1.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>63</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>89</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>41</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "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": {
 
 
 
 
    }
   },
   "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:                Thu, 18 Dec 2025   AIC                            555.927\n",
      "Time:                        07:37:30   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": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res.plot_predict(200, 200 + 2 * 52, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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:                Thu, 18 Dec 2025   AIC                            600.763\n",
      "Time:                        07:37:30   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": {
 
 
 
 
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>AutoReg</th>\n",
       "      <th>SARIMAX</th>\n",
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       "      <th>200</th>\n",
       "      <td>-3.253482</td>\n",
       "      <td>-2.956562</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-8.555660</td>\n",
       "      <td>-7.985588</td>\n",
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       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-13.607557</td>\n",
       "      <td>-12.794072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-18.152622</td>\n",
       "      <td>-17.130963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-21.950370</td>\n",
       "      <td>-20.760472</td>\n",
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       "      <th>205</th>\n",
       "      <td>-24.790116</td>\n",
       "      <td>-23.475510</td>\n",
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       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-26.503171</td>\n",
       "      <td>-25.109628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-26.972781</td>\n",
       "      <td>-25.546789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>-26.141244</td>\n",
       "      <td>-24.728384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>-24.013773</td>\n",
       "      <td>-22.657094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>-20.658891</td>\n",
       "      <td>-19.397351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>-16.205310</td>\n",
       "      <td>-15.072385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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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 -20.760472\n",
       "205 -24.790116 -23.475510\n",
       "206 -26.503171 -25.109628\n",
       "207 -26.972781 -25.546789\n",
       "208 -26.141244 -24.728384\n",
       "209 -24.013773 -22.657094\n",
       "210 -20.658891 -19.397351\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"
   ]
  }
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