Yandex_Market_to_send.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задание: Сделайте прогноз аудитории Музыки на декабрь 2020, подробно опишите свой подход. Для задачи можно использовать данные Яндекс.Радара."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import datetime\n",
    "import matplotlib.dates as mdates\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import itertools\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скачаем с Яандекс.Радара данные о количестве пользователей с августа 2018, обработаем их и оставим только ключевой столбец. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df202001 = (pd.read_csv('202001.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201912 = (pd.read_csv('201912.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201911 = (pd.read_csv('201911.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201910 = (pd.read_csv('201910.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201909 = (pd.read_csv('201909.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201908 = (pd.read_csv('201908.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201907 = (pd.read_csv('201907.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201906 = (pd.read_csv('201906.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201905 = (pd.read_csv('201905.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201904 = (pd.read_csv('201904.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201903 = (pd.read_csv('201903.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201902 = (pd.read_csv('201902.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201901 = (pd.read_csv('201901.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201812 = (pd.read_csv('201812.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201811 = (pd.read_csv('201811.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201810 = (pd.read_csv('201810.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201809 = (pd.read_csv('201809.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])\n",
    "df201808 = (pd.read_csv('201808.csv')).head(1).drop(columns=['Название ресурса', 'Тип ресурса', 'Тематики ресурса', 'Медиахолдинг', 'Данные Метрики', 'URL-адрес ресурса'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df202001['Year'] = 2020 \n",
    "df201912['Year'] = 2019 \n",
    "df201911['Year'] = 2019 \n",
    "df201910['Year'] = 2019 \n",
    "df201909['Year'] = 2019 \n",
    "df201908['Year'] = 2019 \n",
    "df201907['Year'] = 2019 \n",
    "df201906['Year'] = 2019 \n",
    "df201905['Year'] = 2019 \n",
    "df201904['Year'] = 2019 \n",
    "df201903['Year'] = 2019 \n",
    "df201902['Year'] = 2019 \n",
    "df201901['Year'] = 2019 \n",
    "df201812['Year'] = 2018 \n",
    "df201811['Year'] = 2018 \n",
    "df201810['Year'] = 2018 \n",
    "df201809['Year'] = 2018 \n",
    "df201808['Year'] = 2018\n",
    "df202001['Month'] =  1\n",
    "df201912['Month'] =  12\n",
    "df201911['Month'] =  11\n",
    "df201910['Month'] =  10\n",
    "df201909['Month'] =  9\n",
    "df201908['Month'] =  8\n",
    "df201907['Month'] =  7\n",
    "df201906['Month'] =  6\n",
    "df201905['Month'] =  5\n",
    "df201904['Month'] =  4\n",
    "df201903['Month'] =  3\n",
    "df201902['Month'] =  2\n",
    "df201901['Month'] =  1\n",
    "df201812['Month'] =  12\n",
    "df201811['Month'] =  11\n",
    "df201810['Month'] =  10\n",
    "df201809['Month'] =  9\n",
    "df201808['Month'] = 8\n",
    "df202001['Day'] = 1 \n",
    "df201912['Day'] = 1 \n",
    "df201911['Day'] = 1 \n",
    "df201910['Day'] = 1 \n",
    "df201909['Day'] = 1 \n",
    "df201908['Day'] = 1 \n",
    "df201907['Day'] = 1 \n",
    "df201906['Day'] = 1 \n",
    "df201905['Day'] = 1 \n",
    "df201904['Day'] = 1 \n",
    "df201903['Day'] = 1 \n",
    "df201902['Day'] = 1 \n",
    "df201901['Day'] = 1 \n",
    "df201812['Day'] = 1 \n",
    "df201811['Day'] = 1 \n",
    "df201810['Day'] = 1 \n",
    "df201809['Day'] = 1 \n",
    "df201808['Day'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df202001,df201912,df201911,df201910,df201909,df201908,df201907,df201906,df201905,df201904,df201903,df201902,df201901,df201812,df201811,df201810,df201809,df201808])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['My_date'] = pd.to_datetime(df[[\"Year\", \"Month\", \"Day\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['Среднее время','Посетители (кросс-девайс)', 'Посетители (браузер)','Доля пользователей приложения', 'Year', 'Month', 'Day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Дневная аудитория': 'Daily_users'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('My_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>Daily_users</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>My_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>1197057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>1252907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>1300380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>1355755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>1469125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>1399223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>1532277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>1585946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>1623448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>1580869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>1624258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>1641094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1699254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>1823326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>1939963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>1906233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>1960459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1816980.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_users\n",
       "My_date                \n",
       "2018-08-01    1197057.0\n",
       "2018-09-01    1252907.0\n",
       "2018-10-01    1300380.0\n",
       "2018-11-01    1355755.0\n",
       "2018-12-01    1469125.0\n",
       "2019-01-01    1399223.0\n",
       "2019-02-01    1532277.0\n",
       "2019-03-01    1585946.0\n",
       "2019-04-01    1623448.0\n",
       "2019-05-01    1580869.0\n",
       "2019-06-01    1624258.0\n",
       "2019-07-01    1641094.0\n",
       "2019-08-01    1699254.0\n",
       "2019-09-01    1823326.0\n",
       "2019-10-01    1939963.0\n",
       "2019-11-01    1906233.0\n",
       "2019-12-01    1960459.0\n",
       "2020-01-01    1816980.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для получения прогноза аудитории на декабрь 2020 года будем использовать прогнозирование временных рядов.\n",
    "\n",
    "Для дальнейшего анализа воспользуемся декомпозицие временных рядов, чтобы понять характеристики изучаемого ряда."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 18, 8\n",
    "\n",
    "decomposition = sm.tsa.seasonal_decompose(df['Daily_users'], model='additive')\n",
    "fig = decomposition.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция смогла уловить тренд, но не уловила сезонности, хотя о ней можно говорить, судя по графику. Хорошая модель должна была бы предсказать как продолжение тренда, так и появляение сезонности в период ноябрь-февраль.\n",
    "\n",
    "Будем рассматривать модель ARIMA, подберём параметры, которые лучше всего будут подходить для прогнозирования нашего ряда."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples of parameter combinations for Seasonal ARIMA...\n",
      "SARIMAX: (0, 0, 1) x (0, 0, 1, 12)\n",
      "SARIMAX: (0, 0, 1) x (0, 1, 0, 12)\n",
      "SARIMAX: (0, 1, 0) x (0, 1, 1, 12)\n",
      "SARIMAX: (0, 1, 0) x (1, 0, 0, 12)\n"
     ]
    }
   ],
   "source": [
    "p = d = q = range(0, 2)\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]\n",
    "print('Examples of parameter combinations for Seasonal ARIMA...')\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)x(0, 0, 0, 12)12 - AIC:536.6379885530322\n",
      "ARIMA(0, 0, 0)x(0, 1, 0, 12)12 - AIC:152.90062175432288\n",
      "ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:157.93880958273195\n",
      "ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:496.03395253629367\n",
      "ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:117.65141535788611\n",
      "ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:206.68425194022495\n",
      "ARIMA(0, 1, 0)x(0, 0, 0, 12)12 - AIC:409.3079485623749\n",
      "ARIMA(0, 1, 0)x(0, 1, 0, 12)12 - AIC:104.71591093565803\n",
      "ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:130.10171984862265\n",
      "ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:386.391178709278\n",
      "ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:77.01640761665249\n",
      "ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:131.10926868204592\n",
      "ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:433.12952029704485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:129.75681298102714\n",
      "ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:131.61580661314937\n",
      "ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:410.24652112416516\n",
      "ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:87.61875127044702\n",
      "ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:132.34054463276297\n",
      "ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:411.075386795503\n",
      "ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:104.3556959448301\n",
      "ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:106.57744368393998\n",
      "ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:386.7851539048502\n",
      "ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:106.94159186205457\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(df['Daily_users'],\n",
    "                                            order=param,\n",
    "                                            seasonal_order=param_seasonal,\n",
    "                                            enforce_stationarity=False,\n",
    "                                            enforce_invertibility=False)\n",
    "\n",
    "            results = mod.fit()\n",
    "\n",
    "            print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMA(0, 1, 1)x(0, 1, 0, 12) - наилучший набор параметров. Натренируем модель на наших данных с применение оптимального набора параметров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.5096      0.208      2.454      0.014       0.103       0.917\n",
      "sigma2      3.746e+09   1.95e-12   1.92e+21      0.000    3.75e+09    3.75e+09\n",
      "==============================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(df['Daily_users'],\n",
    "                                order=(0, 1, 1),\n",
    "                                seasonal_order=(0, 1, 0, 12),\n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "\n",
    "results = mod.fit()\n",
    "\n",
    "print(results.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_diagnostics(lags=2,figsize=(16, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, насколько точно модель предсказывает на исторических данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = results.get_prediction(start=pd.to_datetime('2019-01-01'), end=pd.to_datetime('2020-01-01') , dynamic=False)\n",
    "pred_ci = pred.conf_int()\n",
    "\n",
    "ax = df['Daily_users'].plot(label='observed')\n",
    "pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 7))\n",
    "\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.2)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Daily_users')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кроме пары выбросов, модель достаточно точно предсказала аудиторию на исторических данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error of our forecasts is 63012556760.65\n"
     ]
    }
   ],
   "source": [
    "y_forecasted = pred.predicted_mean\n",
    "y_truth = df['Daily_users']['2019-01-01':'2020-01-01']\n",
    "mse = ((y_forecasted - y_truth) ** 2).mean()\n",
    "print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем модель для предсказания аудитории в будущем. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_uc = results.get_forecast(steps = 15)\n",
    "\n",
    "pred_ci = pred_uc.conf_int()\n",
    "\n",
    "ax = df['Daily_users'].plot(label='observed', figsize=(14, 7))\n",
    "pred_uc.predicted_mean.plot(ax=ax, label='Forecast')\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.25)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Daily_users')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of predicted daily users on 2020-12-01: 2354705 ±585043\n"
     ]
    }
   ],
   "source": [
    "print('Number of predicted daily users on 2020-12-01: %.0f ±%.0f' % (pred_uc.predicted_mean['2020-12-01'], pred_uc.predicted_mean['2020-12-01']-pred_ci['lower Daily_users']['2020-12-01']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно, полученная модель повторяет тренд исторических данных и влючает в себя ожидаемую сезонность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}