{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2024-04-02 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kolminkertainen eksponentiaalinen tasoitus\n",
    "\n",
    "Aikasarjaennustamisessa oletan että toteutuneiden havaintojen muodostama aikasarja sisältää informaatiota, joka auttaa tulevien havaintojen ennustamisessa. \n",
    "\n",
    "Eksponentiaalisen tasoituksen mallit ovat erityisen suosittuja liiketaloudessa kysynnän ennustamisessa. Mallit ovat helppokäyttöisiä, nopeasti laskettavissa ja helposti päivitettävissä uusien havaintojen myötä. Ennustusmenetelmä riippuu siitä, minkälaista systemaattista vaihtelua aikasarjassa esiintyy. Eksponentiaalisia tasoitusmenetelmiä käytettäessä on kolme päävaihtoehtoa:\n",
    "* **Yksinkertainen eksponentiaalinen tasoitus** aikasarjoille, joissa ei ole trendiä eikä kausivaihtelua. Jos ennustetaan pidemmälle kuin seuraavaan aikaleimaan, niin yksinkertainen eksoponentiaalinen tasoitus antaa kaikille tuleville aikaleimoille saman ennusteen.\n",
    "* **Kaksinkertainen eksponentiaalinen tasoitus** eli **Holt**in menetelmä aikasarjoille, joissa on trendi, mutta ei kausivaihtelua.\n",
    "* **Kolminkertainen eksponentiaalinen tasoitus** eli **Holt-Winters**in menetelmä aikasarjoille, joissa on sekä trendi että kausivaihtelu.\n",
    "\n",
    "Tässä muistiossa käytetään **Holt-Winters**in menetelmää. Holt-Winterin tulomallissa aikasarjan tason **L** (level) hetkellä **t** määrittää lauseke\n",
    "\n",
    "<b>L<sub>t</sub> = alfa * Y<sub>t</sub>/S<sub>t-s</sub> + (1 - alfa)(L<sub>t-1</sub> + T<sub>t-1</sub>)</b>\n",
    "\n",
    "Yllä **Y<sub>t</sub>** on viimeisin havainto, **&nbsp;S<sub>t-s</sub>** on edellisen vastaavan periodin kausivaihtelu ja **&nbsp; T<sub>t-1</sub>** on edellinen trendi.\n",
    "\n",
    "Trendille **T** hetkellä **t** saadaan arvio lausekkeesta\n",
    "\n",
    "<b>T<sub>t </sub>= beta * (L<sub>t</sub> - L<sub>t-1</sub>) + (1 - beta) * T<sub>t-1</sub></b>\n",
    "\n",
    "Kausivaihtelulle **S** hetkellä **t** saadaan arvio lausekkeesta\n",
    "<b>S<sub>t</sub> = gamma * Y<sub>t</sub>/L<sub>t</sub> + (1 - gamma) * S<sub>t-s</sub></b>\n",
    "\n",
    "Ennuste hetkelle **t + p** saadaan\n",
    "<b>(L<sub>t</sub> + pT<sub>t</sub>)S<sub>t-s</sub></b>\n",
    "\n",
    "Yllä on kyse Holt-Wintersin tulomallista, jossa kausivaihtelu huomioidaan kausivaihtelukertoimena. Holt-Wintersin mallia voidaan soveltaa myös summamallina, jolloin kausivaihtelu huomioidaan lisättävänä kausivaihteluterminä. Tulomalli soveltuu paremmin tilanteisiin, joissa kausivaihtelukomponentin suuruus vaihtelee aikasarjan tason **L** mukaan. Summamalli soveltuu tilanteisiin, joissa kausivaihtelukomponentin suuruus ei riipu aikasarjan tasosta **L**.\n",
    "\n",
    "Mallin parametrit **alfa**, **beta** ja **gamma** pyritään määrittämään siten että ennustevirheiden neliöiden summa saadaan mahdollisimman pieneksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from statsmodels.tsa.api import seasonal_decompose\n",
    "from statsmodels.tsa.api import ExponentialSmoothing\n",
    "\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aikasarjaan tutustuminen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Vuosineljännes</th>\n",
       "      <th>Kysyntä</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vuosineljännes  Kysyntä\n",
       "0     2013-12-31      500\n",
       "1     2014-03-31      350\n",
       "2     2014-06-30      250\n",
       "3     2014-09-30      400\n",
       "4     2014-12-31      450"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('http://taanila.fi/aikasarja.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Kysyntä</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vuosineljännes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä\n",
       "Vuosineljännes         \n",
       "2013-12-31          500\n",
       "2014-03-31          350\n",
       "2014-06-30          250\n",
       "2014-09-30          400\n",
       "2014-12-31          450\n",
       "2015-03-31          350\n",
       "2015-06-30          200\n",
       "2015-09-30          300\n",
       "2015-12-31          350\n",
       "2016-03-31          200\n",
       "2016-06-30          150\n",
       "2016-09-30          400\n",
       "2016-12-31          550\n",
       "2017-03-31          350\n",
       "2017-06-30          250\n",
       "2017-09-30          550\n",
       "2017-12-31          550\n",
       "2018-03-31          400\n",
       "2018-06-30          350\n",
       "2018-09-30          600\n",
       "2018-12-31          750\n",
       "2019-03-31          500\n",
       "2019-06-30          400\n",
       "2019-09-30          650\n",
       "2019-12-31          850"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aikaleimat indeksiin\n",
    "# to_datetime muuntaa merkkijonomuotoisen tiedon aikaleimoiksi\n",
    "# format-parametri mahdollistaa erilaisten esitysmuotojen tunnistamisen aikaleimoiksi\n",
    "df.index = pd.to_datetime(df['Vuosineljännes'], format=\"%Y-%m-%d\")\n",
    "\n",
    "# Pudotetaan tarpeettomaksi käynyt sarake pois\n",
    "df = df.drop('Vuosineljännes', axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Vuosineljännes'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aikasarjan vaihtelua aiheuttavien komponenttien erottelu\n",
    "# Observed=alkuperäinen aikasarja, Trend=trendi, Seasonal=kausivaihtelu,\n",
    "# Residual=muu kuin trendiin ja kausivaihteluun liittyvä vaihtelu\n",
    "\n",
    "decompose = seasonal_decompose(df['Kysyntä']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tässä aikasarjassa on selvästi havaittavat trendi ja kausivaihtelu. Koska kyse on vuosineljänneksittäisestä datasta, niin kausivaihtelu esiintyy neljän havainnon (vuosineljänneksen) jaksoissa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mallin sovitus\n",
    "\n",
    "Ennustemalli sovitetaan (**fit**) dataan.\n",
    "Tuloksena saadaan olio (tässä olen antanut oliolle nimeksi **malli**), joka sisältää monenlaista tietoa mallista.\n",
    "\n",
    "Trendiin (**trend**) käytän summamallia (**add**), jossa trendi on aikasarjaan lisättävä termi. Tämä on suositeltavin vaihtoehto.\n",
    "\n",
    "Kausivaihteluun (**seasonal**) käytän summamallia (**add**) tai tulomallia (**mul**). Tulomallissa kausivaihtelu ilmenee kertoimina. Tässä olen kokeillut myös summamallia, mutta tulomalli osoittautui paremmaksi.\n",
    "\n",
    "**seasonal_periods**-parametrille käytän arvoa 4, koska kausivaihtelu esiintyy neljän vuosineljänneksen jaksoissa.\n",
    "\n",
    "**freq**-parametrille käytän arvoa **'QE'**, koska kyseessä ovat vuosineljänneksien viimeiset päivät. Lisätietoa <b>freq</b>-parametrin mahdollisista arvoista https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "malli = ExponentialSmoothing(df['Kysyntä'], trend='add', seasonal='mul', \n",
    "                             seasonal_periods=4, freq='QE').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Kysyntä</th>\n",
       "      <th>Ennuste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vuosineljännes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>500</td>\n",
       "      <td>500.361288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>343.996373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>265.222508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>445.316005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450</td>\n",
       "      <td>496.221285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>313.025004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200</td>\n",
       "      <td>262.293162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300</td>\n",
       "      <td>366.159531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350</td>\n",
       "      <td>377.900949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200</td>\n",
       "      <td>244.302321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150</td>\n",
       "      <td>156.073801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>268.831974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>473.395022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>371.342033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>265.642242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550</td>\n",
       "      <td>445.771871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>654.725518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400</td>\n",
       "      <td>382.808599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350</td>\n",
       "      <td>299.486010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600</td>\n",
       "      <td>609.324360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "      <td>725.597453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>508.968588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>375.853080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>699.824318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>789.985348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste\n",
       "Vuosineljännes                     \n",
       "2013-12-31          500  500.361288\n",
       "2014-03-31          350  343.996373\n",
       "2014-06-30          250  265.222508\n",
       "2014-09-30          400  445.316005\n",
       "2014-12-31          450  496.221285\n",
       "2015-03-31          350  313.025004\n",
       "2015-06-30          200  262.293162\n",
       "2015-09-30          300  366.159531\n",
       "2015-12-31          350  377.900949\n",
       "2016-03-31          200  244.302321\n",
       "2016-06-30          150  156.073801\n",
       "2016-09-30          400  268.831974\n",
       "2016-12-31          550  473.395022\n",
       "2017-03-31          350  371.342033\n",
       "2017-06-30          250  265.642242\n",
       "2017-09-30          550  445.771871\n",
       "2017-12-31          550  654.725518\n",
       "2018-03-31          400  382.808599\n",
       "2018-06-30          350  299.486010\n",
       "2018-09-30          600  609.324360\n",
       "2018-12-31          750  725.597453\n",
       "2019-03-31          500  508.968588\n",
       "2019-06-30          400  375.853080\n",
       "2019-09-30          650  699.824318\n",
       "2019-12-31          850  789.985348"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# malli-olion avulla saan mallin mukaan simuloidut ennusteet (fittedvalues) jo toteutuneille ajankohdille\n",
    "df['Ennuste'] = malli.fittedvalues\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Vuosineljännes'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alkuperäinen aikasarja ja mallin mukaiset ennusteet samaan kaavioon\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Kysyntä</th>\n",
       "      <th>Ennuste</th>\n",
       "      <th>Ennustevirhe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vuosineljännes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>500</td>\n",
       "      <td>500.361288</td>\n",
       "      <td>-0.361288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>343.996373</td>\n",
       "      <td>6.003627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>265.222508</td>\n",
       "      <td>-15.222508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>445.316005</td>\n",
       "      <td>-45.316005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450</td>\n",
       "      <td>496.221285</td>\n",
       "      <td>-46.221285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>313.025004</td>\n",
       "      <td>36.974996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200</td>\n",
       "      <td>262.293162</td>\n",
       "      <td>-62.293162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300</td>\n",
       "      <td>366.159531</td>\n",
       "      <td>-66.159531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350</td>\n",
       "      <td>377.900949</td>\n",
       "      <td>-27.900949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200</td>\n",
       "      <td>244.302321</td>\n",
       "      <td>-44.302321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150</td>\n",
       "      <td>156.073801</td>\n",
       "      <td>-6.073801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>268.831974</td>\n",
       "      <td>131.168026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>473.395022</td>\n",
       "      <td>76.604978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>371.342033</td>\n",
       "      <td>-21.342033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>265.642242</td>\n",
       "      <td>-15.642242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550</td>\n",
       "      <td>445.771871</td>\n",
       "      <td>104.228129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>654.725518</td>\n",
       "      <td>-104.725518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400</td>\n",
       "      <td>382.808599</td>\n",
       "      <td>17.191401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350</td>\n",
       "      <td>299.486010</td>\n",
       "      <td>50.513990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600</td>\n",
       "      <td>609.324360</td>\n",
       "      <td>-9.324360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "      <td>725.597453</td>\n",
       "      <td>24.402547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>508.968588</td>\n",
       "      <td>-8.968588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>375.853080</td>\n",
       "      <td>24.146920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>699.824318</td>\n",
       "      <td>-49.824318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>789.985348</td>\n",
       "      <td>60.014652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste  Ennustevirhe\n",
       "Vuosineljännes                                   \n",
       "2013-12-31          500  500.361288     -0.361288\n",
       "2014-03-31          350  343.996373      6.003627\n",
       "2014-06-30          250  265.222508    -15.222508\n",
       "2014-09-30          400  445.316005    -45.316005\n",
       "2014-12-31          450  496.221285    -46.221285\n",
       "2015-03-31          350  313.025004     36.974996\n",
       "2015-06-30          200  262.293162    -62.293162\n",
       "2015-09-30          300  366.159531    -66.159531\n",
       "2015-12-31          350  377.900949    -27.900949\n",
       "2016-03-31          200  244.302321    -44.302321\n",
       "2016-06-30          150  156.073801     -6.073801\n",
       "2016-09-30          400  268.831974    131.168026\n",
       "2016-12-31          550  473.395022     76.604978\n",
       "2017-03-31          350  371.342033    -21.342033\n",
       "2017-06-30          250  265.642242    -15.642242\n",
       "2017-09-30          550  445.771871    104.228129\n",
       "2017-12-31          550  654.725518   -104.725518\n",
       "2018-03-31          400  382.808599     17.191401\n",
       "2018-06-30          350  299.486010     50.513990\n",
       "2018-09-30          600  609.324360     -9.324360\n",
       "2018-12-31          750  725.597453     24.402547\n",
       "2019-03-31          500  508.968588     -8.968588\n",
       "2019-06-30          400  375.853080     24.146920\n",
       "2019-09-30          650  699.824318    -49.824318\n",
       "2019-12-31          850  789.985348     60.014652"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ennustevirheet (residuaalit) löytyvät malli-oliosta\n",
    "df['Ennustevirhe'] = malli.resid\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mallin tarkastelu\n",
    "\n",
    "Mallin hyvyyden tarkasteluun on monia tapoja. Tässä käytän\n",
    "\n",
    "* ennustevirheiden aikasarjaa viivakaaviona\n",
    "* ennusteiden ja toteutuneiden arvojen hajontakaaviota\n",
    "* malliin liittyvää statistiikkaa, jonka saan **summary**-funktiolla.\n",
    "\n",
    "Huomaa erityisesti **SSE** (sum of squared errors). Mallia laskeva algoritmi yrittää saada SSE:n mahdollisimman pieneksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ennustevirhe')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ennustevirheet aikasarjana\n",
    "# On hyvä, jos ennustevirheiden aikasarjan vaihtelu on sattumanvaraista\n",
    "df['Ennustevirhe'].plot()\n",
    "plt.ylabel('Ennustevirhe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Toteutunut kysyntä')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ennusteiden ja toteutuneiden kysyntöjen hajontakaavio\n",
    "# Ennustemalli on sitä parempi, mitä paremmin pisteet seuraavat suoraa viivaa\n",
    "# vasemmasta alakulmasta oikeaan yläkulmaan\n",
    "df.plot(kind='scatter', x='Ennuste', y='Kysyntä')\n",
    "plt.xlabel('Ennuste')\n",
    "plt.ylabel('Toteutunut kysyntä')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ExponentialSmoothing Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Kysyntä</td>       <th>  No. Observations:  </th>        <td>25</td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ExponentialSmoothing</td> <th>  SSE                </th>     <td>72742.406</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>                <td>True</td>         <th>  AIC                </th>      <td>215.395</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                  <td>Additive</td>       <th>  BIC                </th>      <td>225.146</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>            <td>Multiplicative</td>    <th>  AICC               </th>      <td>231.109</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>           <td>4</td>          <th>  Date:              </th> <td>Tue, 02 Apr 2024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox:</th>                  <td>False</td>        <th>  Time:              </th>     <td>14:29:10</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Box-Cox Coeff.:</th>           <td>None</td>         <th>                     </th>         <td> </td>       \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>                  <th>coeff</th>                <th>code</th>               <th>optimized</th>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_level</th>    <td>           0.8965013</td> <td>               alpha</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_trend</th>    <td>           0.0119557</td> <td>                beta</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smoothing_seasonal</th> <td>           0.0088302</td> <td>               gamma</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>      <td>           304.31607</td> <td>                 l.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_trend</th>      <td>           10.241530</td> <td>                 b.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.0</th>  <td>           1.5906826</td> <td>                 s.0</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.1</th>  <td>           1.0597773</td> <td>                 s.1</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.2</th>  <td>           0.7801315</td> <td>                 s.2</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_seasons.3</th>  <td>           1.3390183</td> <td>                 s.3</td> <td>                True</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}      &       Kysyntä        & \\textbf{  No. Observations:  } &          25           \\\\\n",
       "\\textbf{Model:}              & ExponentialSmoothing & \\textbf{  SSE                } &      72742.406        \\\\\n",
       "\\textbf{Optimized:}          &         True         & \\textbf{  AIC                } &       215.395         \\\\\n",
       "\\textbf{Trend:}              &       Additive       & \\textbf{  BIC                } &       225.146         \\\\\n",
       "\\textbf{Seasonal:}           &    Multiplicative    & \\textbf{  AICC               } &       231.109         \\\\\n",
       "\\textbf{Seasonal Periods:}   &          4           & \\textbf{  Date:              } &   Tue, 02 Apr 2024    \\\\\n",
       "\\textbf{Box-Cox:}            &        False         & \\textbf{  Time:              } &       14:29:10        \\\\\n",
       "\\textbf{Box-Cox Coeff.:}     &         None         & \\textbf{                     } &                       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lccc}\n",
       "                             &    \\textbf{coeff}    &    \\textbf{code}     &  \\textbf{optimized}   \\\\\n",
       "\\midrule\n",
       "\\textbf{smoothing\\_level}    &            0.8965013 &                alpha &                 True  \\\\\n",
       "\\textbf{smoothing\\_trend}    &            0.0119557 &                 beta &                 True  \\\\\n",
       "\\textbf{smoothing\\_seasonal} &            0.0088302 &                gamma &                 True  \\\\\n",
       "\\textbf{initial\\_level}      &            304.31607 &                  l.0 &                 True  \\\\\n",
       "\\textbf{initial\\_trend}      &            10.241530 &                  b.0 &                 True  \\\\\n",
       "\\textbf{initial\\_seasons.0}  &            1.5906826 &                  s.0 &                 True  \\\\\n",
       "\\textbf{initial\\_seasons.1}  &            1.0597773 &                  s.1 &                 True  \\\\\n",
       "\\textbf{initial\\_seasons.2}  &            0.7801315 &                  s.2 &                 True  \\\\\n",
       "\\textbf{initial\\_seasons.3}  &            1.3390183 &                  s.3 &                 True  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ExponentialSmoothing Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       ExponentialSmoothing Model Results                       \n",
       "================================================================================\n",
       "Dep. Variable:                  Kysyntä   No. Observations:                   25\n",
       "Model:             ExponentialSmoothing   SSE                          72742.406\n",
       "Optimized:                         True   AIC                            215.395\n",
       "Trend:                         Additive   BIC                            225.146\n",
       "Seasonal:                Multiplicative   AICC                           231.109\n",
       "Seasonal Periods:                     4   Date:                 Tue, 02 Apr 2024\n",
       "Box-Cox:                          False   Time:                         14:29:10\n",
       "Box-Cox Coeff.:                    None                                         \n",
       "=================================================================================\n",
       "                          coeff                 code              optimized      \n",
       "---------------------------------------------------------------------------------\n",
       "smoothing_level               0.8965013                alpha                 True\n",
       "smoothing_trend               0.0119557                 beta                 True\n",
       "smoothing_seasonal            0.0088302                gamma                 True\n",
       "initial_level                 304.31607                  l.0                 True\n",
       "initial_trend                 10.241530                  b.0                 True\n",
       "initial_seasons.0             1.5906826                  s.0                 True\n",
       "initial_seasons.1             1.0597773                  s.1                 True\n",
       "initial_seasons.2             0.7801315                  s.2                 True\n",
       "initial_seasons.3             1.3390183                  s.3                 True\n",
       "---------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mallin statistiikkaa\n",
    "malli.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ennusteiden laskeminen\n",
    "\n",
    "Ennustettavien ajankohtien aikaleimojen määrittämiseksi:\n",
    "\n",
    "* Ensimmäisen ennustettavan ajankohdan aikaleiman täytyy olla aikasarjan viimeisintä aikaleimaa seuraava aikaleima.\n",
    "* Ennustettavien ajankohtien lukumäärän voin määrittää **periods**-parametrilla.\n",
    "* Ennustettavien ajankohtien frekvenssin on oltava sama kuin mallia sovitettaessa käytetty **freq**-parametrin arvo.\n",
    "        \n",
    "Lisätietoa **freq**-parametrin mahdollisista arvoista https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Kysyntä</th>\n",
       "      <th>Ennuste</th>\n",
       "      <th>Ennustevirhe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vuosineljännes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "      <td>725.597453</td>\n",
       "      <td>24.402547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>508.968588</td>\n",
       "      <td>-8.968588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>375.853080</td>\n",
       "      <td>24.146920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>699.824318</td>\n",
       "      <td>-49.824318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>789.985348</td>\n",
       "      <td>60.014652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste  Ennustevirhe\n",
       "Vuosineljännes                                   \n",
       "2018-12-31          750  725.597453     24.402547\n",
       "2019-03-31          500  508.968588     -8.968588\n",
       "2019-06-30          400  375.853080     24.146920\n",
       "2019-09-30          650  699.824318    -49.824318\n",
       "2019-12-31          850  789.985348     60.014652"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tarkistan viimeisen aikaleiman\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Ennuste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>573.205675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>429.554427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>754.271537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>908.356987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>616.245179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>461.213502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>808.857186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>972.925416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ennuste\n",
       "2020-03-31  573.205675\n",
       "2020-06-30  429.554427\n",
       "2020-09-30  754.271537\n",
       "2020-12-31  908.356987\n",
       "2021-03-31  616.245179\n",
       "2021-06-30  461.213502\n",
       "2021-09-30  808.857186\n",
       "2021-12-31  972.925416"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ennustettavien ajankohtien aikaleimat (alkupäivänä aikasarjan viimeistä aikaleimaa seuraava aikaleima)\n",
    "index = pd.date_range('2020-03-31', periods=8, freq='QE')\n",
    "\n",
    "# Ennusteet kahdeksalle vuosineljännekselle\n",
    "ennusteet = malli.forecast(8)\n",
    "\n",
    "# Ennusteet dataframeen\n",
    "df_ennuste = pd.DataFrame(data=ennusteet, index=index, columns=['Ennuste'])\n",
    "df_ennuste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Vuosineljännes'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Viivakaavio havainnoista\n",
    "df['Kysyntä'].plot()\n",
    "\n",
    "# Ennusteet kaavioon\n",
    "df_ennuste['Ennuste'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Kysyntä</th>\n",
       "      <th>Ennuste</th>\n",
       "      <th>Ennustevirhe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>500.0</td>\n",
       "      <td>500.361288</td>\n",
       "      <td>-0.361288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350.0</td>\n",
       "      <td>343.996373</td>\n",
       "      <td>6.003627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250.0</td>\n",
       "      <td>265.222508</td>\n",
       "      <td>-15.222508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400.0</td>\n",
       "      <td>445.316005</td>\n",
       "      <td>-45.316005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450.0</td>\n",
       "      <td>496.221285</td>\n",
       "      <td>-46.221285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350.0</td>\n",
       "      <td>313.025004</td>\n",
       "      <td>36.974996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200.0</td>\n",
       "      <td>262.293162</td>\n",
       "      <td>-62.293162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300.0</td>\n",
       "      <td>366.159531</td>\n",
       "      <td>-66.159531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350.0</td>\n",
       "      <td>377.900949</td>\n",
       "      <td>-27.900949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200.0</td>\n",
       "      <td>244.302321</td>\n",
       "      <td>-44.302321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150.0</td>\n",
       "      <td>156.073801</td>\n",
       "      <td>-6.073801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400.0</td>\n",
       "      <td>268.831974</td>\n",
       "      <td>131.168026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550.0</td>\n",
       "      <td>473.395022</td>\n",
       "      <td>76.604978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350.0</td>\n",
       "      <td>371.342033</td>\n",
       "      <td>-21.342033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250.0</td>\n",
       "      <td>265.642242</td>\n",
       "      <td>-15.642242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550.0</td>\n",
       "      <td>445.771871</td>\n",
       "      <td>104.228129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550.0</td>\n",
       "      <td>654.725518</td>\n",
       "      <td>-104.725518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400.0</td>\n",
       "      <td>382.808599</td>\n",
       "      <td>17.191401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350.0</td>\n",
       "      <td>299.486010</td>\n",
       "      <td>50.513990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600.0</td>\n",
       "      <td>609.324360</td>\n",
       "      <td>-9.324360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750.0</td>\n",
       "      <td>725.597453</td>\n",
       "      <td>24.402547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500.0</td>\n",
       "      <td>508.968588</td>\n",
       "      <td>-8.968588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400.0</td>\n",
       "      <td>375.853080</td>\n",
       "      <td>24.146920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650.0</td>\n",
       "      <td>699.824318</td>\n",
       "      <td>-49.824318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850.0</td>\n",
       "      <td>789.985348</td>\n",
       "      <td>60.014652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>573.205675</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>429.554427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>754.271537</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>908.356987</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>616.245179</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>461.213502</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>808.857186</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>972.925416</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Kysyntä     Ennuste  Ennustevirhe\n",
       "2013-12-31    500.0  500.361288     -0.361288\n",
       "2014-03-31    350.0  343.996373      6.003627\n",
       "2014-06-30    250.0  265.222508    -15.222508\n",
       "2014-09-30    400.0  445.316005    -45.316005\n",
       "2014-12-31    450.0  496.221285    -46.221285\n",
       "2015-03-31    350.0  313.025004     36.974996\n",
       "2015-06-30    200.0  262.293162    -62.293162\n",
       "2015-09-30    300.0  366.159531    -66.159531\n",
       "2015-12-31    350.0  377.900949    -27.900949\n",
       "2016-03-31    200.0  244.302321    -44.302321\n",
       "2016-06-30    150.0  156.073801     -6.073801\n",
       "2016-09-30    400.0  268.831974    131.168026\n",
       "2016-12-31    550.0  473.395022     76.604978\n",
       "2017-03-31    350.0  371.342033    -21.342033\n",
       "2017-06-30    250.0  265.642242    -15.642242\n",
       "2017-09-30    550.0  445.771871    104.228129\n",
       "2017-12-31    550.0  654.725518   -104.725518\n",
       "2018-03-31    400.0  382.808599     17.191401\n",
       "2018-06-30    350.0  299.486010     50.513990\n",
       "2018-09-30    600.0  609.324360     -9.324360\n",
       "2018-12-31    750.0  725.597453     24.402547\n",
       "2019-03-31    500.0  508.968588     -8.968588\n",
       "2019-06-30    400.0  375.853080     24.146920\n",
       "2019-09-30    650.0  699.824318    -49.824318\n",
       "2019-12-31    850.0  789.985348     60.014652\n",
       "2020-03-31      NaN  573.205675           NaN\n",
       "2020-06-30      NaN  429.554427           NaN\n",
       "2020-09-30      NaN  754.271537           NaN\n",
       "2020-12-31      NaN  908.356987           NaN\n",
       "2021-03-31      NaN  616.245179           NaN\n",
       "2021-06-30      NaN  461.213502           NaN\n",
       "2021-09-30      NaN  808.857186           NaN\n",
       "2021-12-31      NaN  972.925416           NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data, jossa alkuperäinen aikasarja ja lasketut ennusteet\n",
    "df1 = pd.concat([df, df_ennuste])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Lisätietoa</h2>\n",
    "\n",
    "Data-analytiikka Pythonilla: https://tilastoapu.wordpress.com/python/"
   ]
  }
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