{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoregressive Moving Average (ARMA): Artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:24.711970Z",
     "iopub.status.busy": "2021-02-02T07:02:24.711266Z",
     "iopub.status.idle": "2021-02-02T07:02:25.805280Z",
     "shell.execute_reply": "2021-02-02T07:02:25.803981Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:25.811664Z",
     "iopub.status.busy": "2021-02-02T07:02:25.810431Z",
     "iopub.status.idle": "2021-02-02T07:02:27.743407Z",
     "shell.execute_reply": "2021-02-02T07:02:27.744919Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_predict\n",
    "from statsmodels.tsa.arima_process import arma_generate_sample\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some data from an ARMA process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:27.750689Z",
     "iopub.status.busy": "2021-02-02T07:02:27.749146Z",
     "iopub.status.idle": "2021-02-02T07:02:27.769732Z",
     "shell.execute_reply": "2021-02-02T07:02:27.768619Z"
    }
   },
   "outputs": [],
   "source": [
    "arparams = np.array([.75, -.25])\n",
    "maparams = np.array([.65, .35])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:27.776487Z",
     "iopub.status.busy": "2021-02-02T07:02:27.772709Z",
     "iopub.status.idle": "2021-02-02T07:02:27.783427Z",
     "shell.execute_reply": "2021-02-02T07:02:27.784508Z"
    }
   },
   "outputs": [],
   "source": [
    "arparams = np.r_[1, -arparams]\n",
    "maparams = np.r_[1, maparams]\n",
    "nobs = 250\n",
    "y = arma_generate_sample(arparams, maparams, nobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:27.796553Z",
     "iopub.status.busy": "2021-02-02T07:02:27.787589Z",
     "iopub.status.idle": "2021-02-02T07:02:28.451757Z",
     "shell.execute_reply": "2021-02-02T07:02:28.452881Z"
    }
   },
   "outputs": [],
   "source": [
    "dates = pd.date_range('1980-1-1', freq=\"M\", periods=nobs)\n",
    "y = pd.Series(y, index=dates)\n",
    "arma_mod = ARIMA(y, order=(2, 0, 2), trend='n')\n",
    "arma_res = arma_mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:28.457794Z",
     "iopub.status.busy": "2021-02-02T07:02:28.456379Z",
     "iopub.status.idle": "2021-02-02T07:02:28.502422Z",
     "shell.execute_reply": "2021-02-02T07:02:28.503537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  250\n",
      "Model:                 ARIMA(2, 0, 2)   Log Likelihood                -353.445\n",
      "Date:                Tue, 02 Feb 2021   AIC                            716.891\n",
      "Time:                        07:02:28   BIC                            734.498\n",
      "Sample:                    01-31-1980   HQIC                           723.977\n",
      "                         - 10-31-2000                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.7905      0.142      5.566      0.000       0.512       1.069\n",
      "ar.L2         -0.2314      0.124     -1.859      0.063      -0.475       0.013\n",
      "ma.L1          0.7007      0.131      5.344      0.000       0.444       0.958\n",
      "ma.L2          0.4061      0.097      4.177      0.000       0.216       0.597\n",
      "sigma2         0.9801      0.093     10.514      0.000       0.797       1.163\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 0.29\n",
      "Prob(Q):                              0.96   Prob(JB):                         0.86\n",
      "Heteroskedasticity (H):               0.92   Skew:                             0.02\n",
      "Prob(H) (two-sided):                  0.69   Kurtosis:                         2.84\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "print(arma_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:28.510027Z",
     "iopub.status.busy": "2021-02-02T07:02:28.506870Z",
     "iopub.status.idle": "2021-02-02T07:02:28.533085Z",
     "shell.execute_reply": "2021-02-02T07:02:28.534096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-06-30    0.173211\n",
       "2000-07-31   -0.048325\n",
       "2000-08-31   -0.415804\n",
       "2000-09-30    0.338725\n",
       "2000-10-31    0.360838\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:28.541790Z",
     "iopub.status.busy": "2021-02-02T07:02:28.540383Z",
     "iopub.status.idle": "2021-02-02T07:02:29.497364Z",
     "shell.execute_reply": "2021-02-02T07:02:29.498462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "fig = plot_predict(arma_res, start='1999-06-30', end='2001-05-31', ax=ax)\n",
    "legend = ax.legend(loc='upper left')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}