{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SARIMAX: Model selection, missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:29:26.999657Z", "iopub.status.busy": "2026-07-29T17:29:26.999454Z", "iopub.status.idle": "2026-07-29T17:29:28.057566Z", "shell.execute_reply": "2026-07-29T17:29:28.055806Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:29:28.061801Z", "iopub.status.busy": "2026-07-29T17:29:28.061480Z", "iopub.status.idle": "2026-07-29T17:29:31.309578Z", "shell.execute_reply": "2026-07-29T17:29:31.307880Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:29:31.312354Z", "iopub.status.busy": "2026-07-29T17:29:31.311927Z", "iopub.status.idle": "2026-07-29T17:29:31.532724Z", "shell.execute_reply": "2026-07-29T17:29:31.532034Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Download the dataset\n", "df = pd.read_table(\n", " \"https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat\",\n", " skiprows=1,\n", " header=None,\n", " sep=r\"\\s+\",\n", " engine=\"python\",\n", " names=[\"internet\", \"dinternet\"],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "\n", "As in Durbin and Koopman, we force a number of the values to be missing." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:29:31.539693Z", "iopub.status.busy": "2026-07-29T17:29:31.539478Z", "iopub.status.idle": "2026-07-29T17:29:31.553364Z", "shell.execute_reply": "2026-07-29T17:29:31.547762Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Get the basic series\n", "dta_full = df.dinternet[1:].values\n", "dta_miss = dta_full.copy()\n", "\n", "# Remove datapoints\n", "missing = np.r_[6, 16, 26, 36, 46, 56, 66, 72, 73, 74, 75, 76, 86, 96] - 1\n", "dta_miss[missing] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n", "\n", "There are a couple of things to note here:\n", "\n", "- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n", "- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n", "- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:29:31.558296Z", "iopub.status.busy": "2026-07-29T17:29:31.558069Z", "iopub.status.idle": "2026-07-29T17:33:59.021225Z", "shell.execute_reply": "2026-07-29T17:33:59.018074Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import warnings\n", "\n", "aic_full = pd.DataFrame(np.zeros((6, 6), dtype=float))\n", "aic_miss = pd.DataFrame(np.zeros((6, 6), dtype=float))\n", "\n", "warnings.simplefilter(\"ignore\")\n", "\n", "# Iterate over all ARMA(p,q) models with p,q in [0,6]\n", "for p in range(6):\n", " for q in range(6):\n", " if p == 0 and q == 0:\n", " continue\n", "\n", " # Estimate the model with no missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(\n", " dta_full, order=(p, 0, q), enforce_invertibility=False\n", " )\n", " try:\n", " res = mod.fit(disp=False)\n", " aic_full.iloc[p, q] = res.aic\n", " except Exception:\n", " aic_full.iloc[p, q] = np.nan\n", "\n", " # Estimate the model with missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(\n", " dta_miss, order=(p, 0, q), enforce_invertibility=False\n", " )\n", " try:\n", " res = mod.fit(disp=False)\n", " aic_miss.iloc[p, q] = res.aic\n", " except Exception:\n", " aic_miss.iloc[p, q] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n", "\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n", "1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n", "2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n", "3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n", "4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n", "5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n", "1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n", "2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n", "3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n", "4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n", "5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "**Note**: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postestimation\n", "\n", "Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:33:59.023712Z", "iopub.status.busy": "2026-07-29T17:33:59.023498Z", "iopub.status.idle": "2026-07-29T17:33:59.091812Z", "shell.execute_reply": "2026-07-29T17:33:59.090952Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 99\n", "Model: SARIMAX(1, 0, 1) Log Likelihood -225.770\n", "Date: Wed, 29 Jul 2026 AIC 457.541\n", "Time: 17:33:59 BIC 465.326\n", "Sample: 0 HQIC 460.691\n", " - 99 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1 0.6562 0.092 7.125 0.000 0.476 0.837\n", "ma.L1 0.4878 0.111 4.390 0.000 0.270 0.706\n", "sigma2 10.3402 1.569 6.591 0.000 7.265 13.415\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1.87\n", "Prob(Q): 0.96 Prob(JB): 0.39\n", "Heteroskedasticity (H): 0.59 Skew: -0.10\n", "Prob(H) (two-sided): 0.13 Kurtosis: 3.64\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "# Statespace\n", "mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1, 0, 1))\n", "res = mod.fit(disp=False)\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-07-29T17:33:59.093895Z", "iopub.status.busy": "2026-07-29T17:33:59.093685Z", "iopub.status.idle": "2026-07-29T17:33:59.392146Z", "shell.execute_reply": "2026-07-29T17:33:59.391025Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# In-sample one-step-ahead predictions, and out-of-sample forecasts\n", "nforecast = 20\n", "predict = res.get_prediction(end=mod.nobs + nforecast)\n", "idx = np.arange(len(predict.predicted_mean))\n", "predict_ci = predict.conf_int(alpha=0.5)\n", "\n", "# Graph\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "ax.xaxis.grid()\n", "ax.plot(dta_miss, \"k.\")\n", "\n", "# Plot\n", "ax.plot(idx[:-nforecast], predict.predicted_mean[:-nforecast], \"gray\")\n", "ax.plot(\n", " idx[-nforecast:],\n", " predict.predicted_mean[-nforecast:],\n", " \"k--\",\n", " linestyle=\"--\",\n", " linewidth=2,\n", ")\n", "ax.fill_between(idx, predict_ci[:, 0], predict_ci[:, 1], alpha=0.15)\n", "\n", "ax.set(title=\"Figure 8.9 - Internet series\");" ] } ], "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 }