{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting in statsmodels\n",
    "\n",
    "This notebook describes forecasting using time series models in statsmodels.\n",
    "\n",
    "**Note**: this notebook applies only to the state space model classes, which are:\n",
    "\n",
    "- `sm.tsa.SARIMAX`\n",
    "- `sm.tsa.UnobservedComponents`\n",
    "- `sm.tsa.VARMAX`\n",
    "- `sm.tsa.DynamicFactor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "macrodata = sm.datasets.macrodata.load_pandas().data\n",
    "macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic example\n",
    "\n",
    "A simple example is to use an AR(1) model to forecast inflation. Before forecasting, let's take a look at the series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "endog = macrodata['infl']\n",
    "endog.plot(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing and estimating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to formulate the econometric model that we want to use for forecasting. In this case, we will use an AR(1) model via the `SARIMAX` class in statsmodels.\n",
    "\n",
    "After constructing the model, we need to estimate its parameters. This is done using the `fit` method. The `summary` method produces several convenient tables showing the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                   infl   No. Observations:                  203\n",
      "Model:               SARIMAX(1, 0, 0)   Log Likelihood                -472.714\n",
      "Date:                Thu, 18 Dec 2025   AIC                            951.427\n",
      "Time:                        07:37:30   BIC                            961.367\n",
      "Sample:                    03-31-1959   HQIC                           955.449\n",
      "                         - 09-30-2009                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept      1.3962      0.254      5.488      0.000       0.898       1.895\n",
      "ar.L1          0.6441      0.039     16.482      0.000       0.568       0.721\n",
      "sigma2         6.1519      0.397     15.487      0.000       5.373       6.930\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   8.43   Jarque-Bera (JB):                68.45\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.47   Skew:                            -0.22\n",
      "Prob(H) (two-sided):                  0.12   Kurtosis:                         5.81\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Construct the model\n",
    "mod = sm.tsa.SARIMAX(endog, order=(1, 0, 0), trend='c')\n",
    "# Estimate the parameters\n",
    "res = mod.fit()\n",
    "\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out-of-sample forecasts are produced using the `forecast` or `get_forecast` methods from the results object.\n",
    "\n",
    "The `forecast` method gives only point forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009Q4    3.68921\n",
      "Freq: Q-DEC, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# The default is to get a one-step-ahead forecast:\n",
    "print(res.forecast())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `get_forecast` method is more general, and also allows constructing confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "infl       mean   mean_se  mean_ci_lower  mean_ci_upper\n",
      "2009Q4  3.68921  2.480302      -0.390523       7.768943\n"
     ]
    }
   ],
   "source": [
    "# Here we construct a more complete results object.\n",
    "fcast_res1 = res.get_forecast()\n",
    "\n",
    "# Most results are collected in the `summary_frame` attribute.\n",
    "# Here we specify that we want a confidence level of 90%\n",
    "print(fcast_res1.summary_frame(alpha=0.10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default confidence level is 95%, but this can be controlled by setting the `alpha` parameter, where the confidence level is defined as $(1 - \\alpha) \\times 100\\%$. In the example above, we specified a confidence level of 90%, using `alpha=0.10`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specifying the number of forecasts\n",
    "\n",
    "Both of the functions `forecast` and `get_forecast` accept a single argument indicating how many forecasting steps are desired. One option for this argument is always to provide an integer describing the number of steps ahead you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009Q4    3.689210\n",
      "2010Q1    3.772434\n",
      "Freq: Q-DEC, Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(res.forecast(steps=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "infl        mean   mean_se  mean_ci_lower  mean_ci_upper\n",
      "2009Q4  3.689210  2.480302      -1.172092       8.550512\n",
      "2010Q1  3.772434  2.950274      -2.009996       9.554865\n"
     ]
    }
   ],
   "source": [
    "fcast_res2 = res.get_forecast(steps=2)\n",
    "# Note: since we did not specify the alpha parameter, the\n",
    "# confidence level is at the default, 95%\n",
    "print(fcast_res2.summary_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, **if your data included a Pandas index with a defined frequency** (see the section at the end on Indexes for more information), then you can alternatively specify the date through which you want forecasts to be produced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009Q4    3.689210\n",
      "2010Q1    3.772434\n",
      "2010Q2    3.826039\n",
      "Freq: Q-DEC, Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(res.forecast('2010Q2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "infl        mean   mean_se  mean_ci_lower  mean_ci_upper\n",
      "2009Q4  3.689210  2.480302      -1.172092       8.550512\n",
      "2010Q1  3.772434  2.950274      -2.009996       9.554865\n",
      "2010Q2  3.826039  3.124571      -2.298008       9.950087\n"
     ]
    }
   ],
   "source": [
    "fcast_res3 = res.get_forecast('2010Q2')\n",
    "print(fcast_res3.summary_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data, forecasts, and confidence intervals\n",
    "\n",
    "Often it is useful to plot the data, the forecasts, and the confidence intervals. There are many ways to do this, but here's one example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "# Plot the data (here we are subsetting it to get a better look at the forecasts)\n",
    "endog.loc['1999':].plot(ax=ax)\n",
    "\n",
    "# Construct the forecasts\n",
    "fcast = res.get_forecast('2011Q4').summary_frame()\n",
    "fcast['mean'].plot(ax=ax, style='k--')\n",
    "ax.fill_between(fcast.index, fcast['mean_ci_lower'], fcast['mean_ci_upper'], color='k', alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note on what to expect from forecasts\n",
    "\n",
    "The forecast above may not look very impressive, as it is almost a straight line. This is because this is a very simple, univariate forecasting model. Nonetheless, keep in mind that these simple forecasting models can be extremely competitive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction vs Forecasting\n",
    "\n",
    "The results objects also contain two methods that all for both in-sample fitted values and out-of-sample forecasting. They are `predict` and `get_prediction`. The `predict` method only returns point predictions (similar to `forecast`), while the `get_prediction` method also returns additional results (similar to `get_forecast`).\n",
    "\n",
    "In general, if your interest is out-of-sample forecasting, it is easier to stick to the `forecast` and `get_forecast` methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validation\n",
    "\n",
    "**Note**: some of the functions used in this section were first introduced in statsmodels v0.11.0.\n",
    "\n",
    "A common use case is to cross-validate forecasting methods by performing h-step-ahead forecasts recursively using the following process:\n",
    "\n",
    "1. Fit model parameters on a training sample\n",
    "2. Produce h-step-ahead forecasts from the end of that sample\n",
    "3. Compare forecasts against test dataset to compute error rate\n",
    "4. Expand the sample to include the next observation, and repeat\n",
    "\n",
    "Economists sometimes call this a pseudo-out-of-sample forecast evaluation exercise, or time-series cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will conduct a very simple exercise of this sort using the inflation dataset above. The full dataset contains 203 observations, and for expositional purposes we'll use the first 80% as our training sample and only consider one-step-ahead forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single iteration of the above procedure looks like the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept    1.162076\n",
      "ar.L1        0.724242\n",
      "sigma2       5.051600\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Step 1: fit model parameters w/ training sample\n",
    "training_obs = int(len(endog) * 0.8)\n",
    "\n",
    "training_endog = endog[:training_obs]\n",
    "training_mod = sm.tsa.SARIMAX(\n",
    "    training_endog, order=(1, 0, 0), trend='c')\n",
    "training_res = training_mod.fit()\n",
    "\n",
    "# Print the estimated parameters\n",
    "print(training_res.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        true  forecast    error\n",
      "1999Q3  3.35   2.55262  0.79738\n"
     ]
    }
   ],
   "source": [
    "# Step 2: produce one-step-ahead forecasts\n",
    "fcast = training_res.forecast()\n",
    "\n",
    "# Step 3: compute root mean square forecasting error\n",
    "true = endog.reindex(fcast.index)\n",
    "error = true - fcast\n",
    "\n",
    "# Print out the results\n",
    "print(pd.concat([true.rename('true'),\n",
    "                 fcast.rename('forecast'),\n",
    "                 error.rename('error')], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add on another observation, we can use the `append` or `extend` results methods. Either method can produce the same forecasts, but they differ in the other results that are available:\n",
    "\n",
    "- `append` is the more complete method. It always stores results for all training observations, and it optionally allows refitting the model parameters given the new observations (note that the default is *not* to refit the parameters).\n",
    "- `extend` is a faster method that may be useful if the training sample is very large. It *only* stores results for the new observations, and it does not allow refitting the model parameters (i.e. you have to use the parameters estimated on the previous sample).\n",
    "\n",
    "If your training sample is relatively small (less than a few thousand observations, for example) or if you want to compute the best possible forecasts, then you should use the `append` method. However, if that method is infeasible (for example, because you have a very large training sample) or if you are okay with slightly suboptimal forecasts (because the parameter estimates will be slightly stale), then you can consider the `extend` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A second iteration, using the `append` method and refitting the parameters, would go as follows (note again that the default for `append` does not refit the parameters, but we have overridden that with the `refit=True` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept    1.171544\n",
      "ar.L1        0.723152\n",
      "sigma2       5.024580\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Step 1: append a new observation to the sample and refit the parameters\n",
    "append_res = training_res.append(endog[training_obs:training_obs + 1], refit=True)\n",
    "\n",
    "# Print the re-estimated parameters\n",
    "print(append_res.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that these estimated parameters are slightly different than those we originally estimated. With the new results object, `append_res`, we can compute forecasts starting from one observation further than the previous call:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        true  forecast     error\n",
      "1999Q4  2.85  3.594102 -0.744102\n"
     ]
    }
   ],
   "source": [
    "# Step 2: produce one-step-ahead forecasts\n",
    "fcast = append_res.forecast()\n",
    "\n",
    "# Step 3: compute root mean square forecasting error\n",
    "true = endog.reindex(fcast.index)\n",
    "error = true - fcast\n",
    "\n",
    "# Print out the results\n",
    "print(pd.concat([true.rename('true'),\n",
    "                 fcast.rename('forecast'),\n",
    "                 error.rename('error')], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting it altogether, we can perform the recursive forecast evaluation exercise as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "1999Q3  2.552620       NaN       NaN       NaN       NaN\n",
      "1999Q4  3.010790  3.588286       NaN       NaN       NaN\n",
      "2000Q1  3.342616  3.760863  3.226165       NaN       NaN\n",
      "2000Q2       NaN  3.885850  3.498599  3.885225       NaN\n",
      "2000Q3       NaN       NaN  3.695908  3.975918  4.196649\n"
     ]
    }
   ],
   "source": [
    "# Setup forecasts\n",
    "nforecasts = 3\n",
    "forecasts = {}\n",
    "\n",
    "# Get the number of initial training observations\n",
    "nobs = len(endog)\n",
    "n_init_training = int(nobs * 0.8)\n",
    "\n",
    "# Create model for initial training sample, fit parameters\n",
    "init_training_endog = endog.iloc[:n_init_training]\n",
    "mod = sm.tsa.SARIMAX(training_endog, order=(1, 0, 0), trend='c')\n",
    "res = mod.fit()\n",
    "\n",
    "# Save initial forecast\n",
    "forecasts[training_endog.index[-1]] = res.forecast(steps=nforecasts)\n",
    "\n",
    "# Step through the rest of the sample\n",
    "for t in range(n_init_training, nobs):\n",
    "    # Update the results by appending the next observation\n",
    "    updated_endog = endog.iloc[t:t+1]\n",
    "    res = res.append(updated_endog, refit=False)\n",
    "    \n",
    "    # Save the new set of forecasts\n",
    "    forecasts[updated_endog.index[0]] = res.forecast(steps=nforecasts)\n",
    "\n",
    "# Combine all forecasts into a dataframe\n",
    "forecasts = pd.concat(forecasts, axis=1)\n",
    "\n",
    "print(forecasts.iloc[:5, :5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a set of three forecasts made at each point in time from 1999Q2 through 2009Q3. We can construct the forecast errors by subtracting each forecast from the actual value of `endog` at that point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "1999Q3  0.797380       NaN       NaN       NaN       NaN\n",
      "1999Q4 -0.160790 -0.738286       NaN       NaN       NaN\n",
      "2000Q1  0.417384 -0.000863  0.533835       NaN       NaN\n",
      "2000Q2       NaN  0.304150  0.691401  0.304775       NaN\n",
      "2000Q3       NaN       NaN -0.925908 -1.205918 -1.426649\n"
     ]
    }
   ],
   "source": [
    "# Construct the forecast errors\n",
    "forecast_errors = forecasts.apply(lambda column: endog - column).reindex(forecasts.index)\n",
    "\n",
    "print(forecast_errors.iloc[:5, :5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate our forecasts, we often want to look at a summary value like the root mean square error. Here we can compute that for each horizon by first flattening the forecast errors so that they are indexed by horizon and then computing the root mean square error fore each horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "horizon                                                  \n",
      "1        0.797380 -0.738286  0.533835  0.304775 -1.426649\n",
      "2       -0.160790 -0.000863  0.691401 -1.205918 -0.311464\n",
      "3        0.417384  0.304150 -0.925908 -0.151602 -2.384952\n"
     ]
    }
   ],
   "source": [
    "# Reindex the forecasts by horizon rather than by date\n",
    "def flatten(column):\n",
    "    return column.dropna().reset_index(drop=True)\n",
    "\n",
    "flattened = forecast_errors.apply(flatten)\n",
    "flattened.index = (flattened.index + 1).rename('horizon')\n",
    "\n",
    "print(flattened.iloc[:3, :5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horizon\n",
      "1    3.292700\n",
      "2    3.421808\n",
      "3    3.280012\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Compute the root mean square error\n",
    "rmse = (flattened**2).mean(axis=1)**0.5\n",
    "\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `extend`\n",
    "\n",
    "We can check that we get similar forecasts if we instead use the `extend` method, but that they are not exactly the same as when we use `append` with the `refit=True` argument. This is because `extend` does not re-estimate the parameters given the new observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "1999Q3  2.552620       NaN       NaN       NaN       NaN\n",
      "1999Q4  3.010790  3.588286       NaN       NaN       NaN\n",
      "2000Q1  3.342616  3.760863  3.226165       NaN       NaN\n",
      "2000Q2       NaN  3.885850  3.498599  3.885225       NaN\n",
      "2000Q3       NaN       NaN  3.695908  3.975918  4.196649\n"
     ]
    }
   ],
   "source": [
    "# Setup forecasts\n",
    "nforecasts = 3\n",
    "forecasts = {}\n",
    "\n",
    "# Get the number of initial training observations\n",
    "nobs = len(endog)\n",
    "n_init_training = int(nobs * 0.8)\n",
    "\n",
    "# Create model for initial training sample, fit parameters\n",
    "init_training_endog = endog.iloc[:n_init_training]\n",
    "mod = sm.tsa.SARIMAX(training_endog, order=(1, 0, 0), trend='c')\n",
    "res = mod.fit()\n",
    "\n",
    "# Save initial forecast\n",
    "forecasts[training_endog.index[-1]] = res.forecast(steps=nforecasts)\n",
    "\n",
    "# Step through the rest of the sample\n",
    "for t in range(n_init_training, nobs):\n",
    "    # Update the results by appending the next observation\n",
    "    updated_endog = endog.iloc[t:t+1]\n",
    "    res = res.extend(updated_endog)\n",
    "    \n",
    "    # Save the new set of forecasts\n",
    "    forecasts[updated_endog.index[0]] = res.forecast(steps=nforecasts)\n",
    "\n",
    "# Combine all forecasts into a dataframe\n",
    "forecasts = pd.concat(forecasts, axis=1)\n",
    "\n",
    "print(forecasts.iloc[:5, :5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "1999Q3  0.797380       NaN       NaN       NaN       NaN\n",
      "1999Q4 -0.160790 -0.738286       NaN       NaN       NaN\n",
      "2000Q1  0.417384 -0.000863  0.533835       NaN       NaN\n",
      "2000Q2       NaN  0.304150  0.691401  0.304775       NaN\n",
      "2000Q3       NaN       NaN -0.925908 -1.205918 -1.426649\n"
     ]
    }
   ],
   "source": [
    "# Construct the forecast errors\n",
    "forecast_errors = forecasts.apply(lambda column: endog - column).reindex(forecasts.index)\n",
    "\n",
    "print(forecast_errors.iloc[:5, :5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           1999Q2    1999Q3    1999Q4    2000Q1    2000Q2\n",
      "horizon                                                  \n",
      "1        0.797380 -0.738286  0.533835  0.304775 -1.426649\n",
      "2       -0.160790 -0.000863  0.691401 -1.205918 -0.311464\n",
      "3        0.417384  0.304150 -0.925908 -0.151602 -2.384952\n"
     ]
    }
   ],
   "source": [
    "# Reindex the forecasts by horizon rather than by date\n",
    "def flatten(column):\n",
    "    return column.dropna().reset_index(drop=True)\n",
    "\n",
    "flattened = forecast_errors.apply(flatten)\n",
    "flattened.index = (flattened.index + 1).rename('horizon')\n",
    "\n",
    "print(flattened.iloc[:3, :5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horizon\n",
      "1    3.292700\n",
      "2    3.421808\n",
      "3    3.280012\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Compute the root mean square error\n",
    "rmse = (flattened**2).mean(axis=1)**0.5\n",
    "\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By not re-estimating the parameters, our forecasts are slightly worse (the root mean square error is higher at each horizon). However, the process is faster, even with only 200 datapoints. Using the `%%timeit` cell magic on the cells above, we found a runtime of 570ms using `extend` versus 1.7s using `append` with `refit=True`. (Note that using `extend` is also faster than using `append` with `refit=False`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexes\n",
    "\n",
    "Throughout this notebook, we have been making use of Pandas date indexes with an associated frequency. As you can see, this index marks our data as at a quarterly frequency, between 1959Q1 and 2009Q3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
      "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
      "             ...\n",
      "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
      "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
      "            dtype='period[Q-DEC]', length=203)\n"
     ]
    }
   ],
   "source": [
    "print(endog.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most cases, if your data has an associated data/time index with a defined frequency (like quarterly, monthly, etc.), then it is best to make sure your data is a Pandas series with the appropriate index. Here are three examples of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PeriodIndex(['2000', '2001', '2002', '2003'], dtype='period[Y-DEC]')\n"
     ]
    }
   ],
   "source": [
    "# Annual frequency, using a PeriodIndex\n",
    "index = pd.period_range(start='2000', periods=4, freq='Y')\n",
    "endog1 = pd.Series([1, 2, 3, 4], index=index)\n",
    "print(endog1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2000-01-01', '2000-04-01', '2000-07-01', '2000-10-01'], dtype='datetime64[ns]', freq='QS-JAN')\n"
     ]
    }
   ],
   "source": [
    "# Quarterly frequency, using a DatetimeIndex\n",
    "index = pd.date_range(start='2000', periods=4, freq='QS')\n",
    "endog2 = pd.Series([1, 2, 3, 4], index=index)\n",
    "print(endog2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-30'], dtype='datetime64[ns]', freq='ME')\n"
     ]
    }
   ],
   "source": [
    "# Monthly frequency, using a DatetimeIndex\n",
    "index = pd.date_range(start='2000', periods=4, freq='ME')\n",
    "endog3 = pd.Series([1, 2, 3, 4], index=index)\n",
    "print(endog3.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, if your data has an associated date/time index, it is best to use that even if does not have a defined frequency. An example of that kind of index is as follows - notice that it has `freq=None`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2000-01-01 10:08:00', '2000-01-01 11:32:00',\n",
      "               '2000-01-01 17:32:00', '2000-01-02 06:15:00'],\n",
      "              dtype='datetime64[ns]', freq=None)\n"
     ]
    }
   ],
   "source": [
    "index = pd.DatetimeIndex([\n",
    "    '2000-01-01 10:08am', '2000-01-01 11:32am',\n",
    "    '2000-01-01 5:32pm', '2000-01-02 6:15am'])\n",
    "endog4 = pd.Series([0.2, 0.5, -0.1, 0.1], index=index)\n",
    "print(endog4.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can still pass this data to statsmodels' model classes, but you will get the following warning, that no frequency data was found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "/usr/lib/python3/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "mod = sm.tsa.SARIMAX(endog4)\n",
    "res = mod.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What this means is that you cannot specify forecasting steps by dates, and the output of the `forecast` and `get_forecast` methods will not have associated dates. The reason is that without a given frequency, there is no way to determine what date each forecast should be assigned to. In the example above, there is no pattern to the date/time stamps of the index, so there is no way to determine what the next date/time should be (should it be in the morning of 2000-01-02? the afternoon? or maybe not until 2000-01-03?).\n",
    "\n",
    "For example, if we forecast one-step-ahead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "/usr/lib/python3/dist-packages/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4    0.011866\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.forecast(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index associated with the new forecast is `4`, because if the given data had an integer index, that would be the next value. A warning is given letting the user know that the index is not a date/time index.\n",
    "\n",
    "If we try to specify the steps of the forecast using a date, we will get the following exception:\n",
    "\n",
    "    KeyError: 'The `end` argument could not be matched to a location related to the index of the data.'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'The `end` argument could not be matched to a location related to the index of the data.'\n"
     ]
    }
   ],
   "source": [
    "# Here we'll catch the exception to prevent printing too much of\n",
    "# the exception trace output in this notebook\n",
    "try:\n",
    "    res.forecast('2000-01-03')\n",
    "except KeyError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ultimately there is nothing wrong with using data that does not have an associated date/time frequency, or even using data that has no index at all, like a Numpy array. However, if you can use a Pandas series with an associated frequency, you'll have more options for specifying your forecasts and get back results with a more useful index."
   ]
  }
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