{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from datetime import timedelta\n",
"from pandas.plotting import register_matplotlib_converters\n",
"from statsmodels.tsa.stattools import acf, pacf\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"register_matplotlib_converters()\n",
"from time import time\n",
"import os\n",
"data_folder = '../data/'\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# 9: SARIMA Model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def parser(s):\n",
" return datetime.strptime(s, '%Y-%m-%d')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Total | \n",
"
\n",
" \n",
" | Date | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1986-01-01 | \n",
" 9034 | \n",
"
\n",
" \n",
" | 1986-02-01 | \n",
" 9596 | \n",
"
\n",
" \n",
" | 1986-03-01 | \n",
" 10558 | \n",
"
\n",
" \n",
" | 1986-04-01 | \n",
" 9002 | \n",
"
\n",
" \n",
" | 1986-05-01 | \n",
" 9239 | \n",
"
\n",
" \n",
"
\n",
"