{
 "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": [
    "# Yksinkertainen 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 yksinkertaista eksponentiaalista tasoitusta. Yksinkertaisessa eksponentiaalisessa tasoituksessa ennuste lasketaan seuraavasti:\n",
    "\n",
    "<b>    alfa*edellinen havainto + (1 - alfa)*edellinen ennuste</b>\n",
    "\n",
    "Ennuste saadaan viimeisimmän havainnon ja siihen liittyneen ennusteen painotettuna summana. Painokerroin **alfa** on välillä 0 - 1 oleva luku, joka ilmaisee, kuinka suurella painolla edellistä havaintoa painotetaan ennustetta laskettaessa:\n",
    "\n",
    "* Jos alfa on 0, niin ennuste on sama kuin edellinen ennuste.\n",
    "* Jos alfa on 1, niin ennuste on sama kuin edellinen havainto.\n",
    "* Suuret alfan arvot antavat ennusteita, jotka reagoivat herkästi aikasarjassa esiintyvään vaihteluun, koska viimeisimmillä havainnoilla on suuri paino.\n",
    "* Pienet alfan arvot tasoittavat voimakkaasti aikasarjan vaihtelua.\n",
    "\n",
    "Alfan arvo valitaan yleensä siten että ennustevirheiden neliöiden summa saadaan mahdollisimman pieneksi. Voin kirjoittaa ennusteen laskentakaavan myös muotoon:\n",
    "\n",
    "<b>    edellinen ennuste + alfa*(edellinen havainto – edellinen ennuste)</b>\n",
    "\n",
    "Ennustetta siis korjataan jokaisen toteutuneen havainnon jälkeen korjaustermillä <b>alfa*edellisen ennusteen virhe</b>.\n",
    "\n",
    "Huomaa, että seuraavassa esimerkissä yksinkertainen eksoponentiaalinen tasoitus ei ole paras malli, koska aikasarjassa on selkeä trendi ja kausivaihtelu, jotka malli jättää huomiotta!\n",
    "\n",
    "Trendin huomioivan mallin löydät muistiosta https://nbviewer.org/github/taanila/aikasarjat/blob/main/forecast2.ipynb\n",
    "\n",
    "Trendin ja kausivaihtelun huomioivan mallin löydät muistiosta https://nbviewer.org/github/taanila/aikasarjat/blob/main/forecast3.ipynb"
   ]
  },
  {
   "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 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 ja katsotaan aikasarja\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": "markdown",
   "metadata": {},
   "source": [
    "## Mallin sovitus\n",
    "\n",
    "Ennustemalli sovitetaan (**fit**) dataan. Tuloksena saadaan olio (tässä olen antanut oliolle nimeksi **malli**), joka sisältää monenlaista tietoa mallista.\n",
    "\n",
    "**freq**-parametrille käytän arvoa **'QE'**, koska kyseessä ovat vuosineljänneksien viimeiset päivät. Lisätietoa **freq**-parametrin muista mahdollisista arvoista https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "malli = ExponentialSmoothing(df['Kysyntä'], freq='QE').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>388.105892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>424.219676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>400.265302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>351.767228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450</td>\n",
       "      <td>367.334338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>394.014652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200</td>\n",
       "      <td>379.808938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300</td>\n",
       "      <td>321.775666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350</td>\n",
       "      <td>314.747578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200</td>\n",
       "      <td>326.125285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150</td>\n",
       "      <td>285.418393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>241.712154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>292.799501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>375.810872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>367.480422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550</td>\n",
       "      <td>329.563657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>400.709410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400</td>\n",
       "      <td>448.892896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350</td>\n",
       "      <td>433.112731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600</td>\n",
       "      <td>406.288126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "      <td>468.808566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>559.563003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>540.339064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>495.044680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>545.056456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste\n",
       "Vuosineljännes                     \n",
       "2013-12-31          500  388.105892\n",
       "2014-03-31          350  424.219676\n",
       "2014-06-30          250  400.265302\n",
       "2014-09-30          400  351.767228\n",
       "2014-12-31          450  367.334338\n",
       "2015-03-31          350  394.014652\n",
       "2015-06-30          200  379.808938\n",
       "2015-09-30          300  321.775666\n",
       "2015-12-31          350  314.747578\n",
       "2016-03-31          200  326.125285\n",
       "2016-06-30          150  285.418393\n",
       "2016-09-30          400  241.712154\n",
       "2016-12-31          550  292.799501\n",
       "2017-03-31          350  375.810872\n",
       "2017-06-30          250  367.480422\n",
       "2017-09-30          550  329.563657\n",
       "2017-12-31          550  400.709410\n",
       "2018-03-31          400  448.892896\n",
       "2018-06-30          350  433.112731\n",
       "2018-09-30          600  406.288126\n",
       "2018-12-31          750  468.808566\n",
       "2019-03-31          500  559.563003\n",
       "2019-06-30          400  540.339064\n",
       "2019-09-30          650  495.044680\n",
       "2019-12-31          850  545.056456"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# malli-olion avulla saadaan mallin mukaan simuloidut ennusteet (fittedvalues) jo toteutuneille ajankohdille\n",
    "df['Ennuste'] = malli.fittedvalues\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Vuosineljännes'>"
      ]
     },
     "execution_count": 8,
     "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": 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>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>388.105892</td>\n",
       "      <td>111.894108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>424.219676</td>\n",
       "      <td>-74.219676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>400.265302</td>\n",
       "      <td>-150.265302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>351.767228</td>\n",
       "      <td>48.232772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>450</td>\n",
       "      <td>367.334338</td>\n",
       "      <td>82.665662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>394.014652</td>\n",
       "      <td>-44.014652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>200</td>\n",
       "      <td>379.808938</td>\n",
       "      <td>-179.808938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>300</td>\n",
       "      <td>321.775666</td>\n",
       "      <td>-21.775666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>350</td>\n",
       "      <td>314.747578</td>\n",
       "      <td>35.252422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>200</td>\n",
       "      <td>326.125285</td>\n",
       "      <td>-126.125285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>150</td>\n",
       "      <td>285.418393</td>\n",
       "      <td>-135.418393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>400</td>\n",
       "      <td>241.712154</td>\n",
       "      <td>158.287846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>292.799501</td>\n",
       "      <td>257.200499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>350</td>\n",
       "      <td>375.810872</td>\n",
       "      <td>-25.810872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>250</td>\n",
       "      <td>367.480422</td>\n",
       "      <td>-117.480422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>550</td>\n",
       "      <td>329.563657</td>\n",
       "      <td>220.436343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>550</td>\n",
       "      <td>400.709410</td>\n",
       "      <td>149.290590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>400</td>\n",
       "      <td>448.892896</td>\n",
       "      <td>-48.892896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>350</td>\n",
       "      <td>433.112731</td>\n",
       "      <td>-83.112731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>600</td>\n",
       "      <td>406.288126</td>\n",
       "      <td>193.711874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>750</td>\n",
       "      <td>468.808566</td>\n",
       "      <td>281.191434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>559.563003</td>\n",
       "      <td>-59.563003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>540.339064</td>\n",
       "      <td>-140.339064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>495.044680</td>\n",
       "      <td>154.955320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>545.056456</td>\n",
       "      <td>304.943544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste  Ennustevirhe\n",
       "Vuosineljännes                                   \n",
       "2013-12-31          500  388.105892    111.894108\n",
       "2014-03-31          350  424.219676    -74.219676\n",
       "2014-06-30          250  400.265302   -150.265302\n",
       "2014-09-30          400  351.767228     48.232772\n",
       "2014-12-31          450  367.334338     82.665662\n",
       "2015-03-31          350  394.014652    -44.014652\n",
       "2015-06-30          200  379.808938   -179.808938\n",
       "2015-09-30          300  321.775666    -21.775666\n",
       "2015-12-31          350  314.747578     35.252422\n",
       "2016-03-31          200  326.125285   -126.125285\n",
       "2016-06-30          150  285.418393   -135.418393\n",
       "2016-09-30          400  241.712154    158.287846\n",
       "2016-12-31          550  292.799501    257.200499\n",
       "2017-03-31          350  375.810872    -25.810872\n",
       "2017-06-30          250  367.480422   -117.480422\n",
       "2017-09-30          550  329.563657    220.436343\n",
       "2017-12-31          550  400.709410    149.290590\n",
       "2018-03-31          400  448.892896    -48.892896\n",
       "2018-06-30          350  433.112731    -83.112731\n",
       "2018-09-30          600  406.288126    193.711874\n",
       "2018-12-31          750  468.808566    281.191434\n",
       "2019-03-31          500  559.563003    -59.563003\n",
       "2019-06-30          400  540.339064   -140.339064\n",
       "2019-09-30          650  495.044680    154.955320\n",
       "2019-12-31          850  545.056456    304.943544"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ennustevirhe')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FMhlGVTAU+BBCVO/rg43w+ESMyUrGuJyBB0Mm0lYXy/joBMDt9ePtLccUXpF2iKIobXNFO5urN6xzs8cnJnyrBcr4EEJIP9g215JJvXdr7ikvgbo3s8Bn2czhAIA3vjsKnz+xswmx8n1dB8rr7TDpdVg6ZeBM40DMRj1sSQYAQGMC9/Jpd3lwpNEBgAIfQgg5gc8v4ovvA92aI9nmAkJbXY12t+YnYbPxB9ecNAZpFiOOt3Tii+/rFV6VNqzbGdjmWjQhB6nm2HQWzqbuzdgT3OYanh77wmaAAh9CiMptr2xBs8ONVLMBc8ZkRPQ9GclGqcFhfYd2sz4enx+tTg8AYERGMi6bMwIAFTnHQvg211BPc4WjeV3ybnMBFPgQQlTu0+Ax9tMm5MKoj+wlTRAE5AVHV2h5u4udDtLrBKRbjPjxgtEQBODLAw3SVgIZnN1V7Tja5ITZqOtzLtxghLo3J27Gp0ymxoUMBT6EEFX7LIpj7OHybMEC5zbtvsGw+p5Mqwk6nYDRWVacNj4HQKDWhwweO811xsQ8WIN1ObHAjrQ3JPCR9t0yjapgKPAhhKjW0SYHDtbbYdAJA3Zr7ikvTfsnu1jgkxVWJ7Fq4RgAwJotx+B0J/bJocGSa5sLCI2tSNSMT4fLg8MyFjYDnAY++/fvxzXXXIN58+bhpJNOwi9/+Us0NzcDAHbu3IkVK1agpKQEixcvxpo1a7p973vvvYelS5di5syZuOSSS7B9+3YlfgRCSBywba65YzKRlhxdcWl+ApzsYltd4WMUFo3PwajMZLS7vPj3jmqllqZq2ypbUdXaCatJj9MnRhdwDyQnmPFJ1O7Ne6sDhc0FaWYpCIw17gIfl8uF6667DiUlJfjvf/+L//znP2htbcWvfvUrtLW14YYbbsBFF12E0tJSPProo3jsscewa9cuAMCmTZvw8MMP4/HHH0dpaSkuvPBC3HTTTejspOF8hGjRYLe5gLBePhpuYthbxkenE7BywWgAgSLnRG+UNxhsm2vp5DyYjfqY3ndWgk9ol2siezjuAp/q6mpMnDgRt9xyC0wmEzIyMnD55ZejtLQU69evR3p6Oq688koYDAYsXLgQF1xwAVavXg0AWLNmDc477zzMnj0bRqMRV199NTIyMvDBBx8o/FMRQmKt3eXB5iOBTPBgiksTaasru8cn5xVzRiDJoMO+mnZsPdqixNJUy+cX8b60zTX0poU9hbo3J2bgI/eJLgCIXUVWjIwdOxYvv/xyt7/7+OOPMWXKFBw8eBDjx4/vdltRURHeeecdAEB5eTkuvfTSE27fv39/1Ovw+XxRf09/9xOr+0tkdC1jRwvXcsO+Onj9IsblWDEywxz1z5KbEtgaq21zDfo68H4dGzpYcbOx2xptSXpcOGMY1mytwt+/rUDJSPneZCLF+7VkNh1pRn1HF2xmA34wLjPm682wBDJIDR1dmn1e9odlfCYPs0W9/ki/nrvAJ5woinjyySexYcMGvPHGG3j99ddhsVi6fY3ZbIbTGZg+7HA4+r09GmVlZYNfeBzuL5HRtYwdNV/Ldza1AgCmZgI7duyI+vub7IHC3tpWJ7Zv3x5Rx+e+8Hodj9QEMmKO5lrs2NHe7bZ5GR6sAfBhWQ2WjfYiwxzbLZvB4vVaMn/fFnhjnpNvxL7du2J+/3Z3oKGmvcuLzVu3w6TX3vOyL50ePw43BAqbhZZK7NhRJcvjcBv42O123HfffdizZw/eeOMNTJgwARaLBR0dHd2+zuVywWoNzOaxWCxwuVwn3J6REVlTs3DTpk2DXj/0FwKfz4eysrKY3V8io2sZO2q/ll6fH7v+swEA8KNTp2JmhI0Lw3V5fMCHn8DtBwonTBnUgEner6Pnv98AcGPWpGLMnJDT7baZAP7fge+wvbIVu51puG1BkRJLlPB+LYHA8670gy8AAKtOm4yZxTn9f8MgiKII43/Ww+MTMWLcRBSkWwb+ph7UcC17U1rRDBH1yE8zY9GC2VF/P/u5B8Jl4FNZWYnrr78eBQUFeOedd5CZmQkAGD9+PL755ptuX1teXo7i4mIAQHFxMQ4ePHjC7aeeemrUa9Dr9TF9wsT6/hIZXcvYUeu1LD3airZODzKSjZhTmAW9LvpPxcl6PTKSjWhxetDg8CDLFv0bDMPrdWST2XNTLb2u76qFY7C9cgfeKj2OWxYXR9wAUk68XksA+PZwM5odbmQkG3FycS70Ml2vLGsSattdaOn0YmTW4K8Fz9eyN3tq7AAC9T1yrlv5Z3kPbW1tuOqqqzBr1iy88sorUtADAEuXLkVjYyNee+01eDwefPfdd1i3bp1U17N8+XKsW7cO3333HTweD1577TU0NTVh6dKlSv04hBAZsNNcp0/IHVTQw+Rp+GSX3y+iOVggm23rPZt1zrR8ZKeYUNvuwid76+K5PFX6oCxQ1Hz21GGyBons3yvRTnZJjQsL5K054y7js3btWlRXV+PDDz/ERx991O227du349VXX8Wjjz6Kp59+GpmZmbj//vuxYMECAMDChQvx4IMP4qGHHkJdXR2Kiorw0ksvIT09XYGfhBAil8+C/XsiHUral/w0M/bXdmiyl09bpwfe4BT2LGvv/VCSDHpcMXcU/ndDOV7fWIFzp8W2GZ/W7KsJlFqcWpwt6+Owf6+GBOvlwwqbp42QZ1QFw13gc8011+Caa67p8/Zp06bhrbfe6vP2ZcuWYdmyZXIsjRDCgUMNdhxudMCoF3Dq+KG9AYV6+WjvDYYdZU81G6SBrL350fxReO6Lcnx3uBkH6jowPs8WryWqDssMDqbuJhpsbEUiZXycbi8ONQS2uuTs4QNwuNVFCCH9Ydtc8wuzYDNH1625J2mrS4MZH1bfk23rv/ttQboFSycHMmf/oKntffL6/FIGJj/YA0ouOcFePonUvXlvdTtEEchLTUKuTd7rS4EPIURV2JiKJYPo1twTewPT4laX1Lywj22ucFcF53et3XYcHS6PnMtSrUa7Gz6/CL1OOKEhZKyFMj6JE/iUxaFxIUOBDyFENVqdbqnT8FDrewBtj61gb5p9FTaHWzguC0W5KXC4fVi7TZ7eKWrHsoJ5tqQhFdRHIhG7N8djVAVDgQ8hRDW++L4BPr+ICXk2jMxMHvL95Wl4UKm01RVBdkIQwud3VdD8rl7UtgVmPubJvM0FhOZ1sc7biSAeoyoYCnwIIarx6RCGkvaGbXU1Odzo8qqvvX9/QgNKI9uWuWTWcFhNehxqcODbQ01yLk2VWFZwWDwCn+BQ2UTJ+DjdXpTXh3r4yI0CH0KIKri9fnx5oAFAbLa5ACAj2SideKpv19an61Bxc2QdqW1mIy6ZNQJAIOtDuqthW12p8gc+OcGC9GaHG36/9rNv+2ra4ReBXFsScuNwfSnwIYSoQmlFMzpcXmSnmDBzZHpM7lMQBOSlBt5ktLbdFW3GBwBWLgxsd32ytw7VrZ2yrEut4pnxyQxmfHx+Ea2d2i82Lzsev/oegAIfQohKfBqjbs095Wv0SHuTIxD45ESY8QGA8Xk2LBibCb8I/L9NlXItTZVY4BOPjI9Rr0N6cqBVQyKc7CqrCgzQpcCHEEKCRFGMWbfmnkIFztp6g2nsCGx1RZPxAUJH29/cXKm5uqehYIHxsDR5mxcyrM4nEbo376mOX2EzQIEPIUQFyuvtqGx2wqTX4ZQYjwvI1+DJLqfbi05PIGgZqIFhT0sn5yE/1YwmhxsfltXKsTzVEUUxrltdQOhkl9a7N7s8PhyMY2EzQIEPIUQFWNPCheOyYE2K7aQddrJLS718WLbHbNTBaopuyrVBr8OP5o8CQEXOTKvTgy6vHwCQmypv80ImUbo3761ph88vIjslSaq3kxsFPoQQ7rExFbHo1tyTFsdWNIQVNgtC9PVQV8wbCaNewLbKVqm/SiJjz40sqwlJhugCycFKlHldof49qYN6rg4GBT6EEK412buwtTJ23Zp70uLYilDX5sF9gs61mXHO1MCkdsr6xLewmQl1b9Z2xoed6IrXNhdAgQ8hhHMbvm+AKAKTh6XKMhU7fGyFVjoWSz18rJGf6OppVfBo+793VKPVqe2sw0Bq4lzfA4QyPg0d2r728RxVwVDgQwjhmpzbXECoZqPL60ebRnqmSBmfIQzTnD06A5OGpaLL68eaLcdjtTRVkuZ0xTPwsWo/49OtsHkEBT6EEIIurw9fxbhbc09JBr3UME4rdT6NUQwo7YsgCLgqmPX5x3dHE6KDcF/YnK5hcdzqYv2XtFzcvE8qbDZJmdd4oMCHEMKtTYeb4XD7kGNLkrUGIE9jU9rZVle0PXx6WjZzOFLNBlQ2O6VxIYmoNtjjKV+JjI+Gi5tZYfOUgrS4FTYDFPgQQjgmDSWdmAtdDLs195SvsbEVjUMsbmYsJj1WzBkJILGLnFnGJ66BT7DGx+n2wen2xu1x46ksjhPZw1HgQwjhUni35iUybXMxoV4+2thWkAKfIRQ3MysXBLa7vjjQgKNNjiHfnxopUdyckmRAUnCArlazPrvjPKqCocCHEMKl/bUdqGrtRJJBh5OKYtutuSet9fJpcrDJ7ENvCDcm24pF43MgisAb3x0d8v2pjaPLiw5XIOMSz+PsgiBIxelarPNxeXw4UNcBIL6FzQAFPoQQTrHTXCcXZcMSZffhaGlpbIXH50erM3A6bSinusKxo+3/3HIcne7Emt/FguGUJANsZmNcHzs7hRU4ay/j831tB7x+EZlWEwrimEkDKPAhhHDqU5mGkvYmT0NjK5qD2R69TkC6JTZv1KdNyMWIDAvaOj1Yt7M6JvepFnXB50Q863uY0Lwu7WV8wvv3xLOwGaDAhxDCIXuXFzuPtwIAzpCpf0+4PJt2Mj4NHYE3yUyrKWYF4XqdINX6/H1jhWYaPUaC1ffE87g1wya0a3GrK3xURbxR4EMI4U6gizJgMxviUlfBPs03Odzo8qp7K6dRmtM19MLmcJfNGYkkgw57qtuxrbI1pvfNM7bVpUTGh9VoaXGrS6kTXQAFPoQQDtWzTrlx+pSdkWyEKXiCpr5d3Z+u2QmgnBgUNofLsJpwwYwCAMA/Euhoey0HGR9WrK4Vbq9fKmyO94kugAIfQgiH6jpY4BPbN+++CIIgPZbat7vkyvgAwFULxwAA3i+rkbbUtK5GwRofFrw2auxa17W74PGJMBl0GC7D/L2BUOBDCOFOXTDrwmpv4iFfI0fapaPsMTrRFW7aiDTMHJkOj0/E26WVMb9/HrFAWJmMjzbnddWHfbCJd2EzQIEPIYRD7M0mN45vNloZW8GyA7Ho4dMbdrR99aZKeH1+WR6DJ0pmfLI0epxdiQ824SjwIYRwh9XZxGurC9BOL58GGbe6AODcacOQaTWhps2Fr8sbZXkMXri9finbEs+uzQzL2rU43ZoKMuviXMPXEwU+hBDuKPHCKI2t0Ehxs1wZH7NRj0XjcwAAe6vbZXkMXtR3BE4XmvQ6ZMoUSPYnI9kIQQBEEWgJNqXUApbxyY3jB5twFPgQQrgT7+LmwGMFMz5q3+qS5nTJd+3GZlsBAIcbtD27SwrA05SpRTHodchM1l4vn1CND2V8CCEEoiiGPhHGs7g5Tf3FzX6/KHVuzrbJl6EYm5MCADjSaJftMXigZPNChtX5aGlQab30+00ZH0IIQVunB25voJ4hnqnw8FNdau1M3NbpgdcfWHuWjBmfQpbxadR2xkfq4ZMW/yPXDPt31FLGh2p8CCEkDMv2ZCQbkWSQdzhpOBZkub2hIZ9qwwpxU80GqSGjHFjg0+r0oEVjzfXChZoXKpOZAMK7N2sx8KGMDyGEKPZpMMmglwpY1brd1dAhb2EzYzHppYnahzW83VXTzkPGR1vdmzvdPrS7vADi264iHAU+hBCuKNHDh8lTeRPDeBQ2M6zOR8sFzqzQXYmj7IzWujezwmaLUQ9bkkGRNVDgQwjhSn0Ha24W/zQ429JQ68muJhb4yFjYzIzN0X6dDytuVqoWBdBexqcurEeXEiflAAp8CCGcUbLwUe0nu1iHXznGVfTE6nyOaDTj4/eLUnZCyYxPVoq2anzYNY3nic2eKPAhhHBFycLHPJV3b2bFzXKe6GKkrS6N1vg0Odzw+EQIQuwn3UcjW2PH2ZVuXghQ4EMI4UzohVGBjI/K53WFipvjsNUVzPhUNDnh86vz+H9/2HMgJyUJRr1yb5XZYRkftbZZCFev8FF2gAIfQghnlHxhzFP52IpGe/wyPgXpFpgMOri9flS3dsr+ePFWK53oUu4NGgg1MOzy+mHv8iq6llhQ+ig7QIEPIYQjgbqK+A8oZdQ+qJRtdeXEIeOj1wkYk5UMADjUoL3trtq2QDCnZNdmAEg2GZBsCvSz0sJ2V6i4mTI+hBCCZqcbXn+griIeBbo9sTe5ZocbXV5f3B9/qBqDW13xyPgAwNhsNrpCewXOLOOjZGEzw7I+WihwrqPiZkIICWGZliyrMnUV6clGqeNxvcq2u5xuLzo9gWBN7gaGTGGOdoeVSkfZOQh8QnU+6s/4NLQrl9FlKPAhhHCjXuEXRUEQVLvdxbI9ZqMOVlN8Rn2wAmdNZnw4aF7IsAwe28pUK0eXFx1dynZtBijwIYRwROnhhUD3YaVq0hBW2ByvxnBSE0Mt1vhw8FxkWM0WC27VitXvWU16pCjUtRmgwIcQwpE6DtLg0skulR1pD3Vtjt+1YzU+1W0udLrVVxPVF1EUwzI+ys3pYrSS8eHhgw1AgQ8hhCM8FD5KYytUlvGRujZb5T/RxWRYTUhPNgLQ1nZXR5cXzmAgp/SpLkA7xc2hOXzKfbABKPAhhHCEh+ZmoUGl6nqTkTI+cT4Np8U6H5btSbMYYYlTvVR/tFLcXM/BUXaAAh9CCEd42OpiDevUNqi0MY4DSsMVZrMp7dqp8+GpsBkIZXyaNJLxocCHEEKCeHhhVGtxc6Mjvj18GFbgrMWMj9Jv0EyOVjI+weLmXAVnnwEU+BBCOOH1+aWshZI1AHlhgY+aZiM1dsS/uBkIbXUd0lDgU8Ndxifwb9rW6YHb61d4NYMXqvFRYcanubkZr732Gh599FHY7XZs2LAh1usihCSYJocbfjEwCiHeWYtwLPBxe/1odXoUW0e0pK2uOBY3A6Ep7Uca7KoKFPvD01F2AEi3GKHXBVoUNDvUm/WRxtGoLeOzZ88enH322fjoo4/wzjvvoKWlBXfccQfeffddOdZHCEkQ7NNgTkqS9CKvBJNBh6xg8KCm7a4mB5vMHt83ldFZyRAEoN3lldagdmxOFy8ZH51OQKZV3Se7RFHkYisbGETg89hjj+Hee+/FW2+9BYPBgJEjR+LZZ5/FK6+8Isf6CCEJgofCZiZPZXU+Hl8oOxXvU11mox7D0wO9brRS58NO9Ck9mT0cC8bVGlzaw1oEqO44+4EDB7Bs2TIAkLqDnnLKKairq4vtygghCYWX/X9AfSe72PaHXicg3WKM++MXZmurg7M0mZ2jwCcnmMljtVxqwz7Y2MwGJJuU69oMDCLwyczMxOHDh7v93eHDh5GdnR2zRfHA61NvARkhahTq4cNDxiewBrVkfBqCb4aZVhN0CmwTjgvW+RzWQMbH5fGhJZg9G5aqfNdmJpTxUWfgU9/BxzYXMIjA50c/+hF++tOf4p///Ce8Xi8++OAD3HHHHbj88svlWJ9ijrc4lV4CIQlF2upSsGszk6eyQaWN0pyu+BY2M6GMj/oDH/ZvbjbqkGpRNjMRLkvlR9pZ80Klj7IDQNT/qqtWrYJer8ff//53+P1+PPXUU7j88stx9dVXy7A85RxucGBcXprSyyAkYdRx9IlQ6uWjkq2upuCbYY5Cbypa6uVTEzajK17DXiMR6t6szowPL4XNwCCPs1955ZV4//33sWPHDnz88ce49tprodPFviVQc3Mzli5dik2bNkl/t3PnTqxYsQIlJSVYvHgx1qxZ0+173nvvPSxduhQzZ87EJZdcgu3btw/qsbWQsiVETVjGR+nCRyBsUKlKxlbwkvE52uRQfZlAHUdbruFC3ZvVmfHh6fc76oyP3+/HRx99hMrKSni93m633XrrrTFb2NatW3HvvfeisrJS+ru2tjbccMMNuP3223H55ZejtLQUt9xyCyZMmIDp06dj06ZNePjhh/HSSy9h+vTpWL16NW666SZs2LABFkt0e7WHNFKkR4ha8DCni8lX2VaXdJQ9zie6mII0C5IMOnR5/ahq7cToLKsi64iFGo6msofLUXvGh2V0OdjKjjpN8+CDD+LXv/41vvjiC2zatEn6b/PmzTFb1HvvvYef//znuOuuu7r9/fr165Geno4rr7wSBoMBCxcuxAUXXIDVq1cDANasWYPzzjsPs2fPhtFoxNVXX42MjAx88MEHUa9BCylbQtTC7fVLb948BT7NDje6vD6FVzMwpbo2MzqdoJk6H7a9ydOJLkD9GR+ePthEnfH58MMP8c4772DcuHFyrAcAcPLJJ+OCCy6AwWDoFvwcPHgQ48eP7/a1RUVFeOeddwAA5eXluPTSS0+4ff/+/VGv4UijA16vd8h7vD6fr9v/ksGjaxk7vF3LuuDxYaNeQGqSTvF12ZJ0MBl0cHv9qGlxYmRmcq9fx8t1bAhmATKSDYqtpTA7GftrO3CovgOnFmdF/f28XMua4HMxN8Wk+FrCZQQLrZscXQO+N/FyLcOx7Gl2ilG2dUV6v1EHPjabDYWFhVEvKBo5OTm9/r3D4Thhy8psNsPpdEZ0ezQcXT58vnEbspL1UX9vb8rKymJyP4SuZSzxci0PNAU+xaYnCdi5c6fCqwnISBJQ5wX+u203JmX3Xzuj9HU83tAKAGivq8KOHY2KrMHiDZQHbN5/FCXW1kHfj9LX8nBNMwCgs7kGO3a0KLqWcG5fYByIxyfim9LtSDENvGGj9LVkRFGUeiM1HT+EHS3KnpaL+tHPP/98vPrqq7juuuvkWE+/LBYLOjo6uv2dy+WC1WqVbne5XCfcnpGRMajHS8odjZlFQ+tP5PP5UFZWhmnTpkGvj00QlajoWsYOb9eybk8tgGaMyE7FzJkzlV4OAGBU6SbUOVpgyx2JmdOH9fo1vFxHx0cbAHgxd/pETBuuzGnUw2IV1u4vQzssg/o35OVadnwcmD25cMYkTB/B18nelPc/hb3Li4LC8dKMtN7wci2Z9k4P3L5Ak+PT5pUgySjPmtjPPZCIA5/FixdDEAR4vV7U1dXhr3/9K1JTU7t9zWeffRb9SqMwfvx4fPPNN93+rry8HMXFxQCA4uJiHDx48ITbTz311EE93uFGJxZNiM0/kF6v5+IJqAV0LWOHl2vZYA80jMtPNXOxHgDIT7MAaEGD3T3gmpS8jn6/KHVuzkuzKLaOcbk2AEBFo3NIa1DyWnp9fmmQ5vCMZG6ei0yOLQn2Li+anV4UR7A2Xn6/Gx2BXZc0ixHJZmVOHoaLOPC57bbb5FxHRJYuXYo//vGPeO2113DllVdi69atWLduHZ577jkAwPLly3HLLbfgnHPOwezZs7F69Wo0NTVh6dKlg3q88no62UVIPPDU44PJZ92bOe/l09bpgdcf2AZRcqr92GBxc227C44uL6xJ/DT/i1Sj3Q2/GBj9kaXQCbn+ZFlNONLoUN28Lp7m8AFRBD4XX3wxAOCmm27CH//4R6Sk9J1mk0tGRgZeffVVPProo3j66aeRmZmJ+++/HwsWLAAALFy4EA8++CAeeugh1NXVoaioCC+99BLS09MH9XgHKfAhJC546vHBqGVQKRthkGo2wGSIfT+1SKUnm5BpNaHZ4caRRgemKrTlNhSssDnPlgS9AqM/BsJOdqntSDtvH2yiDsm3b98Okyl+qarvv/++25+nTZuGt956q8+vX7ZsmTREdagOUeBDSFzUc9Tjg5EGlXIe+DR0BHv4cDAKYGy2Fc0ONw6rNPBh/9a8HWVnslU6toL18Mnl5Pd7UMXNt99+Oy644ALk5OR0O1I3d+7cmC5OaU0ON1ocbmQo1A2VkETB2ydCIGxsBeeBD/v0n63gNhczNseKLUdbcESlvXxqOO3hw7DttyaVZXzq1brVxbzxxhsAgC+++KLb3wuCgH379sVkUTwoSDPjUFMnyhvsmGvNVHo5hGgabzUAQPig0i6IosjV3KZw7E0w26b8B7TCbDalXZ3Zchbk5nM0lT1cjkq3uuqljA8fv99RBz6DaQaoRoU5KYHAp96OuWMo8CFELi6PD22dgVNduRxlfFjg4/b60eL0IJPTzC/b9lBqXEU4tQ8rDXVtVv5a9iaU8VHZVpf0wYaP3++IA5/a2lrk5+ejurq6z68pKCiIyaJ4EDih0EAnuwiRGUuDm406pJr5OQlkMuiQZTWhyeFGbZuL28CHFTcreaKLGRs2toLnLFlfQltdfGZ82BBatWV82FY2Lx9sIn6VOffcc7Ft2zapn48oBo5Psv+vta0u9smFAp/B8/tF6Dg8GUH4Ig0vTDVz90aZl2pGk8ONunYXJhekDvwNCggVNysfmI3KSoZOAOxdXjTYu7gpZo2UVNzMyRt0T6yAXU0ZH1EU1Vvj8/777wOQv0khL1hXTAp8oieKIu7/1278e0c13r3pB5iQb1N6SYRjUmEzh2+S+Wlm7K1p57rAmaeMT5JBjxEZyahsduJwg0NVgY8oimGT2flcNytg7+jywuXxwSxTB+RYanV64Pb5AQQaMPIg4qYPw4YFWra/+uqr6OjowPDhw0/4T0tYyraqtRNOt1fh1ajLXz49iNWbKmHv8uL9XX1vjRIC8NnDh5F6+XDcxJBte+RwkPEB1Fvn0+r0wO0NvEHz+FwEgFSLAUZ9ICuqliaGLKObaTUhycBHoBZ1t6umpiZcfvnluOSSS/Dmm2+eMDtLKzKsJmlP/7BKj2Yq4Z2tx/H0Z6GxIaUV/Az5I3yq5/AoO5MvneziOPDp4Ke4GQAKpTofdWXLWbYni6M36J4EQZAye2o50s62uXg50QUMIvB58skn8d///hfLly/He++9h1NOOQW//OUvUVpaKsf6FFVE211R+aa8Efe+uwsAcF5wqOP2Yy3wBNOchPQm1MOHnxdGhp3u4XWry+n2otPjAwBuRiywMgG1ZXxq2wNdm3nt4cOorXszb4XNwCACHwCw2Wz40Y9+hH/+8594/vnnsWXLFqxatSrWa1PcuFwKfCJ1sK4DN76xFV6/iPOnD8PTV5QgI9kIl8ePPdXtSi+PcIy3o67heN/qYtkes1EHq4mPLEX4yS41qW0LPA95LWxm1Na9mQ19zVNzxgcAHA4H3n33XaxcuRI33ngjpk2bhpdffjnWa1NcEQU+EanvcOHqv5Wiw+XFnNEZ+NOKGdDrBMweHeh/VHqkWeEVEp7x1s4+HO9jKxrDCpt5ORHHanwqm52qyvbWtqkr46OWk108dmWPumnGz372M3z++efIz8/HihUr8NRTTyEzU5sN/qTAR2V71fHkdHtx3d+3oKq1E4XZVry4ao500mDumAx8uq8OpRXNuP7UsQqvlPCKt6Ou4din/xanB11eH3e1H40drGszP9cuz2aGxahHp8eHY81OaeuLd7WcH2VncqSMj7q2unj6/Y468DEYDHjppZcwZ84cOdbDFRb4VDQ64PH5YdQrN/mYRz6/iNvf3IFdx9uQkWzE366e263J29zCQEC85WiLKpuZEfnZu7ywdwVOTfJUA8CkWYxIMujQ5fWjvr0LIzOTlV5SN1LXZo6aK+p0Agqzrdhb044jjQ7VBD68z+liQhkftQQ+7NQmP9c16nfy3//+95gzZw727t2L9evXw+12o6mpSY61Ka4gzYxkkx5ev4ijTU6ll8OdR97fi0/31cFk0OGlVXMwJri3z0wtSEOSQSdNayakJ3aiy2rSIyWJn67NjCAI0hshjwXO0pwuTgqbmcIc9dX5sMzEME67NjPsVJdqanw43OqKOvBpbm7GFVdcgcsuuwz33HMPjh07hiVLlmD79u1yrE9RgiBgHJ3s6tWr/z2Cv31TAQD482UzMKeXeWYmgw4zR6YDoDof0jueC5sZngucGzkaUBpuHCtwVtEHnhrO53QxbFtTDVtdfr+IBrsGjrM/8sgjGD9+PEpLS2EwGDBu3DjccMMN+MMf/iDH+hTHtrsOUZ2PZP2eWjz8/l4AwD1nT8T50/ue0cYGvFI/H9IbaWozR/v/PfHcy6cx2MSOh67N4UIZH3W8bjq6vOhwBbZceZ3TxbB5XWpoYNjidMPjC4y34qVrMzCIwGfTpk247777YLFYpJqN6667DuXl5TFfHA/oZFd3O4+14va3tkMUgR/OG4UbF/VftByq86GMDzkRjyc+epK2unjM+HBY3AwAY7PV1cuHbWOmJBm43HINxwKIZocbfr+o8Gr6xzK62Skmrmpko16J0WiEyxV4krBBpQ6HA1artb9vUy3a6go51uzET/6+BS6PH4vG5+DhZVMGLFieNSodOgE42uSU9noJYVS11cXh85d96uepuBkIZXzqO7rQ4fIovJqB1aqksBkAMpID/9Y+v4jWTr6vLa+tKqIOfBYvXoxf/OIXqKiogCAIaGpqwm9/+1uceuqpcqxPceFbXbxH13Jq6/Tg2tdK0WjvwqRhqXj2ylkwRBDB28xGTMwPTLWm7S7Sk9TVlbOMRTiut7rsfGZ8Us1GqeBaDVkfKfDhOABnTAYd0ixGAPzX+dRzeJQdGETg87Of/QzJyck4++yz0d7ejpNPPhmdnZ34+c9/Lsf6FDc6KxkGnQCn24caDl/44sHt9eOmN7biYL0dealJePXqOVGlg+eOyQAAlFbQdhfprl4FGR9ex1Z4fH60OgOf+Hk71QWEOjirIvBpV0/GBwhsHQH8Bz68ZnSj3sy0Wq14+umn0dTUhKqqKuTn5yM3Nxd2uza3gox6HcZkW1Feb0d5vR3D0/kufIs1URRx39oyfHuoCVaTHq9ePTfq455zCzPx941Hqc6HnIClwnl7YQyXJ2V8urjqR9Uc3ObS6wSkBzMAPBmbY8XmimYcUsGRdpbxGaaSwCcrJQmHGhzcd2/mcU4XMIiMz7x58wAAWVlZmD59OnJzcwEAp512WkwXxpNEHlb6zOfleHfbceh1Av73ylmYUpAW9X3MCY6u2FvdLjWrI0QURS67uvbE6hPcXj9anPzUVDQEC5szrSbodHwEY+EKVZTxYUfZeQ7Aw6mlezOb08XbVnZEGZ+jR4/igQcegCiKsNvtJwwktdvtSE1NlWWBPCjKTQH2JF7g89724/jzJwcAAP+zbApOn5A7qPvJTzNjZKYFx5o7se1oC04dnxPLZRKVand54fIEZjnxVvwYzmTQIctqQpPDjdo2V7fu5Epqko6y87GenljHZjUcaWeT2dWT8VHHvC4emxcCEQY+o0ePxplnnomWlhZs27ZNyvowJpMJixcvlmWBPJAKnBMo8Nl4qAm/fGcXAOCni8biyvmjh3R/c0dn4lhzFbZUNFPgQwCEXhRTzQZYOJks3pe8VDOaHG7UtbswuYCPD3nsKDtP/VHChWd8eNoi7A2bzM7bG3RfQt2b+c741HE6hy/iGp8rr7wSADBixAhcdNFFcq2HS4k2rLS8vgM//ccWeHwizps2DPecNXHI9zm3MBNrt1fRyS4i4bXwsTf5aWbsrWnnqsC5kdNxFcyozGTogwdD6tq7uC0cdnv90rVUS8aHdermeWyFL6xrM2+/41HX+CxduhSvvfYaAKC8vBwrVqzAT3/6U9TV1cV6bdwYG+xJ0exwSwWFWtXQ0YWr/1aKdpcXs0al44nLZsSkfoCd7Np+rAUen3/I90fUTw3NCxkex1bwvtVlMugwMiNwEOJwI78fGln3cJNex8025kBYxqfJwW/Gp8nRBZ9fhE7g7zkadeDz8MMP47333gMAPPTQQygoKEBaWhoeeuihWK+NG8kmg3SaS8t1Pp1uH657fQuOt3RidFYyXlo1B2ZjbLYgxuWkICPZCJfHj91VbTG5T6JudSoYV8Hw2MuH167N4UJ1PvwWOLNgNi8tievtuHA5Nv6Ps9e3hzKSkfR8i6eoj7Nv3rwZa9euRVtbG7Zt24YNGzYgPT0dJ598shzr40ZRbgqqWjtRXm/HvMITB3Kqnc8v4s63t2PnsVakJxvxt6vnIiuGKXRBEDB7dCY+3VeHLRUtKBmVEbP7Juqkhh4+DI+9fBo5z/gA6jjZxf5Nh6Wqp1WJlPHheKuL5zl8UYdhDocD6enp2LhxI0aOHIm8vDwIgqCaSHmwtD6z67EP9uHjPXUw6XV4adUc6ZNaLM0rpEaGJETa6uI4Y8HwuNWljowP/8NKQxkf/gNwhp3qcrp9cLr5bBEi1fBxeGIz6oxPcXExnnvuOXz11Vc4/fTTYbfb8eSTT2LKlClyrI8bWi5w/u/BRrz83yMAgD9dNkOaqB5rc8awgaUt3J/yIPJTU40PK8zlaauL1XdkczaZPZwaMj41KmteCASGqSYZdOjy+tFkdyM5k7/Bqrw2LwQGkfF56KGHsHHjRqSkpODWW2/F3r17sWnTJjzwwANyrI8bWj7SzjIwy2YW4MIZBbI9ztSCNCQZdGh2uFXRzZXIi30i5PGFsSdW49Pi9MDl8Sm8GsDvF6VtDnbCh0dsyPOxlk64vXweaqhVUQDOCIIgnebjtc6H16PswCAyPkVFRfjHP/4h/XnevHlYt25dTBfFI9a9uaq1E44uL6xRzKri3eHgp7HJw+TtT2Iy6DBzZDo2HWnGlopmKZgkiUcURanzMI8vjD2lWYzSJ+z69i6MykpWdD1tnR54g0OTszjO+OTakmA16eFw+1DZ7OTyd15t4yqY7BQTqlo7uT3SzmvzQmAQgc///u//9nnbrbfeOqTF8CzDapK6tx5ucGDaiOhHN/DqSPCoKUtLy2numExsOtKM0ooWXDFvlOyPR/jU6vTAHWxrwGsDvnCCICA/zYyjTU7UtrsUD3zYNleq2QCTga8TM+EEQUBhjhW7q9pxuMHOdeDDa5+hvrDDJ028ZnykOXz8/X5HHfhs2rSp259bW1tx6NAhnH322TFbFK/G5aag6Ugzyhs6NBP4iKKII8FtJzkKmnuaW5gJbAANLE1w7EUx02pCkoHvrs1MXmoo8FFaQwfb5uLvTaWnsdkp2F3VzmWdj98fmheXz2Fmoj/sNB/vW108jqOJOvAJ3+Zi/v3vf58QEGlRUW4KNh9p1tTJrvqOLjjcPuh1AkZlyv8pdtaodOgE4GiTE/XtLlXUd5DYC70o8v/GzUi9fDg42aWGwmaGZZJ57OXT6OiCN9hkTw2Zx3As6OVxq8vr80uZKE0cZ+/NsmXL8Nlnn8XirrimxSnth4Kn1EZmWOKSMreZjZiYH6glovEViUtNJ7oYthXCQ8YndJSd38Jmhh1p5zHjU9cWarJn5KzJ3kBYxqeJw2kCTQ43/CKg1wlc1qDF5F968+bNSE5Wds87HrTYy4e9GMWjvodh4yuon0/iChU+8vei2Beplw8PgQ870cXpnK5wY7OD3Zs5HFtR06auqezhpFNdHfxtdbEPNjkpSdDHYORRrEW91bV48eJu/Vc8Hg8aGxtx0003xXRhPGKBz9EmJzw+v+o+IfTmcBzre5i5hZn4+8ajVOcToQ6XBwadjvsJ5tFQ04BShsetLh4/TfdUGMz4NNrdaOv0IM1iVHhFIWrMPDIs8OFxXhfPR9mBQQQ+t912W7c/63Q6jBs3DlOnTo3Zong1LM0sHc082uRAUa5N6SUNmRIZnzmjA40M91a3o8Plgc3Mzwshb1qdbpz95NdIMurw2d2LuJt5M1g8NzfrC09jK0LFzfxvdaUkGZBrS0J9RxeONDowc2S60kuSqLF5IcO6N/NY48P773fUgc/FF18sxzpUQRAEjMtNwa7jbSivt2sq8GH78PGQn2bGyEwLjjV3YntlK04dnxO3x1abF748LL3RHml0oDhP/c85AKhjPXxUVFDKsgL17V2Kdx5XU8YHCLy+BAIfO1eBD/vdyk9Tz5wuhmV8WpxueH1+rj4U8b6VHXXgU1dXh+effx4VFRXw+7t34nz99ddjtjBeFeWEAh+1c3v9qGx2Agjtw8fL3NGZONZchS0VzRT49KG+3YXXvj0i/XlvTbtmAh+em5v1hR3Ldfv8aHa4YzrEN1rsCHOOCjI+AFCYnYLvDjdzd7Ir1MOHzzfo/mQkGyEIgCgGOorzdCqN56PswCACn/vuuw+NjY04/fTTYTQm3hbFOA0VOB9rccLnF5Fs0sc9Mp9bmIm126voZFc/nt1QDpcn9OFib007ls0cruCKYsPvF1Hfob4aH5NBh+wUExrtbtS2u5QNfDrUU9wMAONy+DzSLgU+KprMzhj0OmQkm9DscKPR3sVV4FPPcfNCYBCBT1lZGT7++GNkZsozyJJ3WhpWyl6ECrOtcU/bs5Nd24+1wO31c919VgnHmp34f5srAQDnTx+G/+yqwb6aDoVXFRtNDjd8fhGCEGi7ryZ5qWY02t2oa3dhSoEyTUydbi86g/PClAy+oiH18uHoSLsoimFbXeoJwMNlpwQCnybO6nx4n8MX9buNzWaDyaSuF6tYCg0rdcAfnJWjVvEcVdHTuJwUZCQb4fL4sae6Le6Pz7unPjsIj0/EyUXZ+MnJhQACxeBawAofs1OSuKpLiAQ72VXbptxJGpbtMRt1sKrkpB87NXqk0c7N62a7ywunOxBAqq1rM8NqvHjr3ixlfDjd6or6Vefmm2/Gfffdh127dqG6urrbf4lgdGYyjHoBnR4fqoM9INRKiaPsjCAImB083bWFtru6Ka/vwNptxwEAPz9rAibk2yAIgRe3Bg57dkSL9zR4f/I4aGLYGFbYrGSBdTRGZFhg0AlwefxcnIoDQgF4msWo2lYRoe7N/LwueHx+6aQZr7/jUW913X///QCATz75RPqlYycc9u3bF9vVccig12FMlhUH6+0or7djRIZ6GzeytPNYBTI+ADCvMAOf7qtDaUUzrj91rCJr4NGfPzkAvwgsnZwnnYApzLLicKMD+2rakWNTdzG41OOD00+D/eGhl0+oazOfbyq9Mep1GJWVjMMNDhxucKAgXfmaGjUfZWd47N7MPpwZ9QIykvncHYo68EmE0RQDKcpNkQKf0ybkKr2cQQtlfJQJfOaMCWZ8jrYofjyYF7ur2vBBWS0EAfjZmeOlv580LFUKfNR+Co73Hh/9yeegezN7k8u28vmm0pex2VYcbnDgSKMdJxdnK70cKXhVa30PEKqR46l7s/T7bTNDx2HXZmAQgc/w4eo/VTJUUp2Piguc210eKT2qRI0PAEwtSEOSQYdmhxuHGhzSdU1kf1r/PQBg2YwCaaYZAEwuSMX7ZTXYW6P+Oh/eu7r2h2111Sm51dURmi+lJmNzUoB99TjEycmuGulEl5oDH9a9mZ+MDzuxydMps56iDnw2bdqE3/72t6ioqIAodi9SS4StLkAbM7sqgttcObYkxTonmww6zByZjk1HmrGlojnhA5/NR5rxxfcNMOgE3LlkfLfbJg0L9O/Zp4HAR409fBhpq0vJwMeungGl4dgHLF6Glda2B2o01ZzxYaf6eKrx4b15ITCIwOfxxx/HjBkzcP/998NgiPrbNWFcsBj4YL1dtVs04UfZlTSvMBObjjSjtKIFV8wbpehalCSKIv748X4AwGVzR2JMj3+XScMC2Z9DDQ64PD6YjeosxgSAOhUXN7PAp8Xpgcvjg1GBQ2mNwU/3aunazIyVjrTz8YGxVhMZn2CND0fH2dUwhy/qyKWiogJvvfUWkpLU9UsXS+NyUiAIQKvTgyaHW3UpZyBU2DxOofoehtX5JPqk9i8PNKC0ogUmgw63Ly4+4fb8VDMyko1ocXpwsM6OaSOU6SETC7x3de1PqsUAs1EHl8eP+vYuDE+P/+++GoubgdCw0uMtnejy+pBkUDZ4r9FEjU8o48PLh3A1DH6N+vPKmDFjUF9fL8daVMNi0mN48FSCWre7Djco18Mn3KxR6dAJQGWzU9HtAyX5/SL++HGgtmfVgtG9vhALgiBlfdS83eX1+aW0PM8vjH0RBEHxAme1FjfnpCTBlmSAKAJHm5xKL0d6vVFz4MMGlXZ5/bB3eRVeTQCbw5fLcWAedeBzzjnn4LrrrsNLL72Ef/3rX93+SyRqr/MJTWVXtq7GZjZKRbyJ2s/noz212FPdDqtJj5tOG9fn17HAR80Fzo12N0QR0OsE6Siu2uQpHPiEanz4fWPpjSAIUtZH6dEVLo8PLU4PAGCYCsdVMMkmA5KDPYh42e5SQw1f1Ftdb731FgDgzTff7Pb3giDgoosuismi1KAoJwVffN+gysBHFEVFprL3ZV5hJvbWtKO0ohnnTR+m9HLiyucX8UTwJNdPThnb7wgCLWR8Qkddk7g96joQliFQopePx+dHa/ANW41b7GOzrdh1vE3xOh/2PLQY9Ui1qLtWNSvFBGdzJxrtXSfUBioh1K6C3+dnxP/iW7duxezZs/H555/3evvLL78cs0WpgZqPtNe1d8Hp9kGvEzCSgwaMc8Zk4LVvKxKyzue97VU41OBAerIR151S2O/XTg7L+PCynx8tNffwYZTc6moObnPpdQLSLeobEs0yzEcUzviE1/eo8fcoXHZKEo41d0rdkpXU5Q1l0nhuUBrxVtf111/f7c/Lli3r9ufnnnsuNitSCTVvdbH6nlGZyVwMB50THF2xr6YdHS6PwquJny6vD3/55AAA4KZF45A6QFuBotwUGPUCOlxeVLWqc1wK2//PU9k2TbhcBQMf1hU302pSZcaMZZiVHlaqhRNdDE/zutjz06TXIT2Z38A84ne9nj17es7m6nm71rHAp6bNxU1RWaQON/JxlJ3JTzNjZKYFfhHYXtmq9HLi5u3SY6hq7USuLQmrFo4Z8OtNBp3USkGtk9rVsP8/ECXHVjRJR9nVWR/FSy8ftU9lD8fTkfbQVHa+58hFHPj0/CEG+rPWpSebpCfcIZVlfY4oPKOrN3MT7Fi70+3F05+VAwBuO6M44iGJkwuC210qndRep4LmZgPJTwusXYmMT6MKuuL2hwU+zQ43Wp3KvVHXauAoOxPq3qx8xkctH2yU3+dQMfbpW23bXdJRdg4Km5lEC3z+/u1RNNq7MCLDgsvnjIz4+yarvMA59ImQ7xfG/rAX9fr2rrhnutmbmxoLmwHAmmSQMmZKbnfVamBAKcOOtPOw1aWWDzYU+AyBVOejsgLnUMaHnxERc8dkAAB2HGuF2+tXeDXyauv04IUvDwEA7loyPqo6K+lkV61aAx91fCLsD2u86Pb50eyMb00aK2BV61YXEFbno2CBc40GnodMqIkhB1tdHepoThrxqS6v19utV4/H4+n2Z5/PF8t1DUlTUxN+85vfYPPmzdDr9bjwwgtxzz33xHzEhhoLnN1eP461BApjeTjKzozLSZE6E++pbkPJqAyllySbV74+jLZOD4pyU3BRSXRDf1ngc7TJiQ6XR7E5a4PFBhjy/omwPyaDDtkpJjTa3XGv81Fr1+ZwhdlWfHuoCUcUPNJe2xZ4DaSMT2yp4Sg7EEXgk52djaefflr6c0ZGRrc/Z2VlxXZlQ3DnnXciLy8PX3/9NRobG3HTTTfhtddew3XXXRfTx5GOtKso8KlsdsLnF2E16bnqrCkIAuaMycQne+tQWtGs2cCn0d6Fl/97BADw8zPHQx/lyZxMqwn5qWbUtrvwfW2HNPJDDbq8Puk4Ns9HXSORl2oOBD7tLsTzmdqo8uJmIDilHcplfLw+v3T6SAunuqQaHw4yPg3SqU2+r2vEgU9f/Xt4c/ToUWzevBlfffUVLBYLRo4ciZtvvhl//OMfowp8IslgFWYFeuAcbXais8vT65YFux9eMmLldYEtkjHZVvj9fG0pzR6Vjk/21mHzkWb85KQxJ9zO27UcjOc2HITT7cPUglQsmZgzqJ9lYr4Nte0u7KlqQ8nIwc3sUuJa1gWP4Jv0AmxJOlX/O+bZkrAHQHVbJzKS4ncdG4MDXjOTjaq9fmMyA52SDzfYu/0M8XpO1ra54BcBg05AusWg2uvIZAQbMLZ1eqT3IaVeK1ntVE6KMs/PSB9T3S0re3Hw4EGkp6cjLy9P+rtx48ahuroa7e3tSE1Njeh+ysrKBvwaURRhMQjo9Ir46JutGJXW97ZDJPcXD998H/iUla53Y8eOHcoupoc0d+ATy6ZDDdi+fXufJwV5uZbRanT68I+NDQCAi8fpsXPnzkHdT6YuMOfov3uOYKp5aMXg8byW3zcF/n3Tk3SD/tl5YfAEsrxl5ZWYMsUWt+tY2xL4/W2qrsCOzqq4PGasueyB9h9HGuzYtn07dD1+z+W+lgek56GAsl3qfh4CgF8UoRMAvwh8XbodWZbQCdF4v1bWBJ+fzdUV2GE/HtfHjobmAh+HwwGLpfvsFfZnp9MZceAzbdo06PUDHzEu3rgRu6raYMwaiZlT80+43efzoaysLOL7k9vbh3cD6MDs4uGYOfPEKeBKmuz147dff4oOtx+pI4qlU3MMb9cyWr96bzc8fmB+YQauOmveoFtAVOlrsHb/TtS7TZg5c+ag7kOJa1m7uxZAM0Zk2wa9bl5Mbi7H+sPlEM1pAPxxuY5+v4j2d9cDAH4wa5pq61N8fhHG9evh9onIGzMRwzMCr8/xek6y5+HInFTVPw+ZrI82oMHehfzRxZhSkKrI77fL44N9TS0AYNHcGUhVoLM4+7kHornAJzk5GZ2d3bvasj9brZEX8+r1+oieMEV5KdhV1YbDjc5+vz7S+5NbRXAq8rhcGxfrCWfR6zFzZDq+O9yMrZVtGJ/f+zYOL9cyGkcaHXhnW+AT+i/OmjikQvspw9MBAN/XdQCCLuo6oXDxvJYNwRqE/DSz6v79eipID2xz13e4ARjich3bXW54/YHj87mpFuj16jyUq9cDo7OsKK+3o6K5E6N6nC6V+1rWB5+HBWkW1T8PmawUExrsXWh2err9TPH8/W5qDdT3mI06pFs10sBQLYqLi9Ha2orGxkbp7w4dOoT8/HzYbLaYP57aTnax4YA8HWUPp9V+Pk9+egA+v4jTJ+QMuSB5TJYVZqMOLo8fFU3KdsCNhlqOukYijw0qjWMTQ9bDJ9Vs4GLUzFCw5qmHFWgFwupQtHCUneGhwLmugw0g5n/+mbp/e3oxZswYzJ49G7/73e9gt9tx7NgxPPfcc1i+fLksj1ekoiaGbZ0eqdfDmGzlh5P2hgUFWypaFF5J7Oyracf/7QyMePnZmROGfH96nYCJ+err4KyFHj6MNLaiPX5HiBs6Ar+7aj7KzrDmqUqMrqjRUPNCRhpboWD35vp29bSq0FzgAwBPP/00vF4vzjjjDFx22WU45ZRTcPPNN8vyWCzjc7jRDr+f73llFcEXmVxbErf9X2aNSodOCBy7j+enaTk9sf4ARBE4b/owTB0+uFNYPU1SYQdnNb0wDoQFPq2dHnT54vN7L3Vttqr/+o3LZq+b8Q982KiRPA0FPlkcNDEM9fDh/7pqrsYHOLHnkJxGZSbDpA9sO1S1dmJkJp+ZFCC0zcXLcNLe2MxGTBqWij3V7SitaMb50wuUXtKQbKtswaf76qATgLuXjo/Z/U4eFti2VVPgo6WMT6rFIG03tnTG6yg7a16o3h4+TKGC3Zu1NK6CCXVvVi7jw7a6eO/hA2g04xNPBr1O2jbifbvrSPBFZmwOn/U9zFwNbXf96ePvAQDLZ4844ZTaULBhpWqa0q6WOT6REARByvo0dcanHxabzK7WOV3hWI1PdVsnXJ749XsRRTE0mV0DATgT6t6sXMZHTRldCnxiQC0Fzoc4nMremznBuV1qL3D+prwR3x5qgkmvw+1nxLZ1wIRgjU9tu0vqhsyzTrcP7a5A/xY1pMIjwTJXzfHK+AQ/zWdpYKsr02pCqtkAUURcC/RbnB5pFiDvYxWiIdX4KJnxUVFGlwKfGFBLgXMo48N34MMyPvtq2tHhiu8QyFgRRRF/CGZ7fjR/FEZkxHYLNCXJgNHBzuFq2O6qD6bBLUY9bEna2GHPT2OBT3wyPqHiZvVvdQmCoMjoCrbNlZ1iQpJBG0fZAU5OdalkThdAgU9MjFPBlHa/X5ROUPBc4wMEPjGMykyGXwS2VbYqvZxB+XRfPXYea4XFqMfNp4+T5TEm5aunwLkuLA3O+1HXSOXHOePDipu1kPEBQpnneJ7sqm0P9HRTQ1YiGqy4ucnRBVFU5pAN2+pSQ7sKCnxiIHyrS6kn3UDqOlzo9Phg0AlcF2AzbLtriwq3u/x+UartueakMbK9ELA6HzUcaVfTiY9I5cW5xodtdeVoIOMDhDLPh+L4gVGLR9mB0NBaj09Ee6c37o/vdHvR0RV4XKrxSRDjclIgCN375PCGpZNHZSbDqIKOr2puZLhuVzW+r+uAzWzAT0+VJ9sDhI6071VFxkc9+/+RCm11xSnjY9dOcTMAFAaPtMcz41OnweaFAGAO20JuUKDOh2V7kk16pKhgK5v/d0AVMBv1GBGcN8Nrnc9hlWxzMXODGZ8dx1qlYkQ18Pj8+PMnBwAANy4ah7Rk+folTQoeaT/UYOf+GtUHj2LnaaD5HhPP4man2wunO/A4WRoJfMaGHWmPV6ZcqxkfIHSyS4kC5/APNmrYyqbAJ0akAmdO63zUUtjMjMtJQUayES6PH7ur25ReTsTWbDmOo01OZKeYcPUPxsj6WMPTLUg1G+DxiThYz/exdi1nfFo6/bI3L20MFjabjTpYTdooyh2TFXgtauv0oMUZn0MMtRp8HjJSgbMCpzxD42jUEZRT4BMjrM7nELcZH9a8kO8ePowgCGHjK9Sz3fXe9uMAAtkeq8wpX0EQwjo4qyPwUcOJj0jl2pIgCIBXBJqd8r7ZNIYVNqvhE3UkLCY9hqcHMuXxmtkVal5oicvjxVOol48SW13qCigp8IkR3nv5sH10tWR8gNB2V6lKGhn6/aJUaLxofE5cHlMtoytCzc3U8cIYCaNeJxWVyj2zK9S1WTuBIxDaeo/X6AqpeaEGt7qyFRxbIX2wUcnzkwKfGOE58Ony+nCs2QmA/+aF4cIzPryelgtX2eyEw+1DkkEXt1qqySoJfLS41QWEfh6558pJXZut2jjRxYyN4+gKe5cXHcEmmloMfLIUHFtRp7IPNhT4xEhRTqDQtLbdxV3TvWPNTvjFQNO7HJVE5AAwtSANZqMOLU5PXI+8DhY7XTUx3wZDnE7OSUfaa9q5DQ7tXV44goW5avlEGKkxwSaScvebkjI+GilsZgqlXj7y/36zbS5bkkEVJ4+ipWT3ZtagVC1b2RT4xEhaslF6UTqkwOC9/rD1FGZbVVUfYDLoMHNkOgB1bHftCRZhs2AkHopyU6DXCWh1eqQ0Pm9YNsSWZJC97inelkzKBQB8vKdO1sBTyvhopIcPE8/uzXUanMoeTsnuzWrbyqbAJ4aKcgOfXnjb7lJLx+beqKmfD6vvmVyQFrfHNBv1GBfcLuB1u0uLhc3M6RNyYdAFalQOyvh736ChOV3h2Nb70SYnfDKfjNPyUXYg1MRQma0udW1lU+ATQ7zW+bATE2oqbGbmqGhSO9vqYnU38cIej9cOzmr7NBgNm9mAGXmBYOSDshrZHkerxc0F6RaYDDq4fX5UtXbK+lhqe3OOFntuxDvjo8atbAp8YojXYaVqzvjMGpUOnRAoHJa7gHQoGu1dqGvvgiAEanziifcj7Vp/w1kwIvBi/9HuWtkeQ6vFzXqdINVJyd3BuaYtEFhpNeOTHcwGdnR50eWJTzdxQJ1b2RT4xFBRbqiTLk/Y/vm4HHX08AlnMxulN/YtR/nN+rBsS2G2Ne6//LwfaWcnPrS41QUAcwvMMOgE7K/tkO3Nm21faC3jAwBjg73F5D7SzoqbtXiiCwBSLQYY9YEazng2MWSBT46Kfr8p8IkhttV1tMmBLm/8Iu7+tDk90i/BGBVmfIBQnQ/XgY9C21xAKPA50uSA0x3/AYUDqQue+MhTwdTmwbCZdFgwNvAc/XB37Le7PD4/WoOdjbV2qgsACoNb8BWNTlkfR+rho9HMoyAIUg1YPHv5NEjjaNRzXSnwiaG81CSkJBngF+X/JY7UkabApyi2NjVik9q3VrQqu5B+7JEKm+Mf+OTYkpBjS4IoAvtr+dvuUltX18E4e0o+AODDsthvdzUHP7jodQLSLfLNflPK2Dg1MdR6xgdQZl5XaCtbPUE5BT4xJAgCxnFW4MwKm9VY38OwjM++2nY4PXwO49zLjrIrkPEB+N7uCjU3U88LY7SWTs6FTgDKqtqkZqGxwj5RZ1pN0OnU044iUlITQxkDH7fXL2VBtJrxAZSZ16W25oUABT4xx1uBc2hUhfrqe5i8VDNGZSbDLwLfN/HVHBIITM5mL9pT4niUPRyb1M5b4COKouaLm4HAGw4L0D/eE9usD3sTy9JYYTPDanxq21xweeX5YMOegya9DpkavY5A+Lyu+Nf45Kro95sCnxiTjrRzUuDMCpvVNKqiN2y7a39j/JtzDWR/bQdEMbTlpARej7S3d3rRFXwzU1PX8ME4d9owAMCHMT7dxY6ya/X6ZVhNSE8ObOHV2OWpjQw1L9TOkNfehDI+8dvqqldhRpcCnxjjrZfPYRUOJ+2NtN3VyF/GR2pcqNA2V/hj76/tgF/mRnDRYIXN6clGmI16hVcjr7OCdT5bj7ZI9SSxwN7EtFjYzLAPZtUd8hTnS80LU7U3lT1caGxFHDM+HerL6FLgE2Ms8DncYJe9E+lA/H5RmoFTmK3erS4gFPgcbHLDLVM6fLDYia4pChQ2M4XZVpgMOjjdPlTGuMZkKKRP2io68TFY+WlmzBqVDiC2211s20KrW11A6PWpukOejE8iFDYDiPuprvCtbLU0LwQo8Im5kRkWmPQ6dHn9qGqRtxPpQGrbXXB5/DDoBIzIUPcnnXE5VqRbjHD7+Tu5tFfBE12MQa+TGifu5ajOR+s9fHo6Zyrb7ordsXatdm0OxzLScmV8pKPsGg98pO7NcSpu7ujywhU8cJKrog83FPjEmEGvk36JyxuUfYNm9T2jspJhjNO0cLkIgoAZIwOFwzuOtSq7mDA+v4j9tcpvdQHApHz+TnYlQmFzuLOnBra7Nh9pjtnMpEaNFzcDkObNyZ7x0fjzMN7zulirilSzARaTeray1f1uyClejrSzbS61FzYzJcFJ7ds5CnyONNrh8viRbNJjTJay15nHk131KuzxMRQjM5MxbXga/CKwfk9dTO4zETI+oa2uUDF8LCVMxidYB9bscMMvyl9qocaj7AAFPrLg5Uj7YQ0cZQ9XEqyf4CnjwxoXThqWqniPFR5ndklbXSpKgw8Vy/rEartLKm7W2GT2cGOyk5GRbITTK+LetWUxL9BPlBofdlTfLwJ2dzwCH3VmdCnwkQEvJ7vYVpeamxeGmzEiDQKAyubOuKVyB8LDiS5mUrDGqKq1E61OPo79h058aPdNu6dzgoHPxkNNQ/538PtF6YROtk27W11JBj2evHwG9ALwfztr8MQn38fsvv3+UAGuVgeUMiaDDmnB7t6tLvnHJqm1ho8CHxmEBz5iHNKNfZGaF2ok8LGZjRieGhi7saOyVdnFBEkzuhQsbGZSzUapiJ2XrE+99MKo7TeccGNzUjAx3wavX8Qne4e23dXu8sAbzH5kaTjjAwAnF2XjxtmB36NnNxzCm5srY3K/jY4ueP0idAKQo+GWAAw70t7WJf/p19CJLnX9flPgI4PCbCt0AtDu8sa1g2a4Lq8Px1sCx5oLVd7DJ9z4zMCnme3HlB9YKoqilPFR8ih7OJ5GV/j9IupV2OMjFth210dDbGbIMpupZgNMBu2/XC8uTMatp48DANz/r9344vv6Id8n2+bKsSXBoPJDHpHICgZ3bS75A596lWZ0tf8sUIDZqMfIzGQAwEGFtrsqm5zwi4AtyaCpTznjswKBz7ajrcouBIE0b5PDDb1OwPg8m9LLARDWwZmDwKfF6YbHF8hWaOk5GAl2rP3rg43ocA2+6WZDB9vmSpzrd+cZRbikZDh8fhG3rN6GPcE5eIOVKCe6mHhmfOqpuJmEYwXOhxQaXXGI1ffkWDXVon1CMPDZebxV8QaRe2sCL8jjcqzcdCXmKePD9v+zrKaEyFaEG5+XgrE5Vrh9fny+f/BZi0QobO5JEAQ8ful0LBybBYfbh2tfK0V16+B7oiXKiS6GneyKy1YXZXxIOFbnc6hevonD/dFafQ8zPNWAlCQ9nG4fDtQpW8cS2uZSZjBpb1jG52CdHR6fsh2u2YtiItX3MIIgSEXOH5YNfrsrdJRdu4XNvTEZdHhh5WwU56agrr0L175WivZBZs4SLePDasHk3uoKdG1W56lNCnxkMk7hYaWHG7QxqqInvSBg+oh0AMB2hQucpcJmDk50MSMyLLAlGeD2+aVTfUpJtB4+PbHtri8O1MPpHlxHYtaBV8tzuvqSZjHib9fMRY4tCftrO3DL6m2DCuZDR9nV3b0+UuyElVxdsJm2To80PohOdREAYRkfhd58WMZHS4XNzMxgB+ftlcoWOO/hYFRFTzqdgInD2OiKodVGDJXU3ExlnwZjZUpBKkZkWODy+PHl9w2Dug9W3Kz1E119GZGRjL9dPRfJJj2+PtiIX60ti/qkbG2CHGVnTi7KBhAY6DyULcKBsN/vjGQjkgx8bPVHigIfmbDAp76jCw5P/LccDmt0qwsAZgU7OG9TMPDpcHlwtClwao6njA/ATyPDugTP+AiCgHOnsdldg9vuChU3J9ZWV7ipw9Pw7I9mQScAa7YexzOfl0f1/Szjo7YC3MEamZmM+YUZEAH8e2e1bI+j1qPsAAU+skk1G6VptVXt8qYce2p1utEcTJFrpXlhuBnBwOdQgwNtzsGfmBkKNii1IM2MDM5mKPFS4FyXgD18emLH2j/bVweXJ/qGcqy4OVEzPszpE3PxP8umAgD+/MkBrN12PKLvE0URNW2JlfEBgItnDgcArN1WLVsvufoOdTYvBCjwkRXL+hyPc+DDtrnyU82wJhni+tjxkGk1YUxWoF3AjuOtiqxhT1VgG4mnbS5GOtJe3a5oA81E7eETbuaIdOSnmuFw+/Dfg41Rfz/b6spJ4IwP8+MFo/HTRWMBAPe8uwvflg98PdtdXnQGA85EOdUFBAJukz6Q+d95XJ4tb7WOqwAo8JGVFPjINHG4L1obVdGbklEZAJSr8+GxsJmZkG+DTggUxjZ0KDfaI9G3uoBAzVVodlf0213SuIoELG7uzT1nTcT504fB4xPx0ze2Dniyk21zpScbuWk5EQ82swHzhwcCkne3RpYdi5aaDy9Q4CMjpTM+YzVY2MywgaVKnewKjarg5yg7YzbqpaBXqUaGPr8oBV1q/EQYS+xY+yd7a6VTMJFwur1wugMfmrIo8AEQCCT/tGIG5o7JQIfLi2v+Viq9AfdG6uGTgM/B00YHTrGt21WNLm/sP3yrdTI7QIGPrFgTw3jX+BxuZEfZtRv4zArL+MR6kvNAPD4/DtQGrjEvoyp6YgGZUgXOTfYu+EVAJwQaGCayOWMykZ1iQrvLi42HmyL+PpbtMRt1sJoSJ1sxELNRjxdXzsHYbCuqWjtx7d9L4ejq/TW2ti1wqimRtrmYaXkm5NmS0Or0YMMQmmj2RerTRcXNJJx0ssvhQ9cgChsHi211jcvRVg+fcBPybTAbdWh3eaUTbPFSXm+H2+eHzWyQhoLyZpJ0pF2ZjA/7NJidkhjzkfqj1wk4cwqb3VUT8fc1hB1l11L39VjIsJrwt2vmIstqwu6qdtz25nZ4e+nxk4iFzYxeELBsZgEA4N1tVTG//3qVTmYHKPCRVY4tCTazAX4AR4JHn+Xm94uoaNJ+jY9Rr8P04ekA4l/nwzo2Tx6Wyu0bktInu9Rc+CiHc4PNDNfvqev1Dbo3oa7N6ntjiYfRWVa8dNUcJBl0+Hx/PR5at+eEYv5Efx5eXBIIfDbsr0eTPXb1fqKo7gHEFPjISBAEFAXrbHYPcdBepGraXXB5/DDqBW6zEbEi1fkca43r4/LYuLAnVnR9uME+qGPUQ6XWGT5ymT82E+nJRjQ53Nhc0RzR90hdmxN8q7A/s0Zl4KkrSiAIwBvfVeLFrw53uz2RMz4AMD7PhmnD0+D1i1gXw54+LU6PqgcQU+Ajs1OKA100/7VdvkZS4dioilGZyZrfYgid7GqN6+Oyjsg8nuhicm1JyLKa4BeB72vjX+dDPXy6M+p1WDopDwDwUYSnu6SMjwrfWOLp7Kn5uP+8yQCAxz7cj//sCr3WJlrzwt5cMivQ0yeW210sk6bWAcTqW7HKLJ81AgKAjYebcbRJ/loUaVSFxmZ09YZlfL6vbYe9j+LGWBNFMbTVxXHGRxAERbe7pKOuKix8lMs501idT21EBflSxod6+AzoJycX4uofjAEA3P3PndgSzKqFxlVoO/vdnwtnFMCgE1BW1Razwc5S12aVBpQU+MhseIYFM/MDL1xvlx6T/fFChc3are9h8lLNGJ5ugV8EdsWpkWFVayfaXV4Y9QKKc21xeczBYgXOSgQ+1MPnRCcVZcOWZEB9Rxe2Hxu4Lq0hwed0Res350/GmZPz4Pb6cd3rW7C3uh2twc7uiXicnclKScJpE3IBAO9G2PF6IPXSUXZ1Pjcp8ImDMwoDXYbXbD0ecWHjYB1u1H5hc7iZce7nw+p7inNt3Kd4WUZKiZNdau7xIZckgx5nTAq8AX1QNvB2FxU3R0evE/DUFSWYMTIdrU4PfvzKJgCAxahHqkV7HeyjsXx2YLvrX9ur4ItB+486lWd0+X7l1og5BYF6i4aOLnwuQz+FcEeCPXzGavgoe7iS4NyueAU+atjmYthW1/6ajriPrmAnPtR41FVOZwdPd320u3bAfxMqbo6exaTHK1fNwajMZGle4bA0M7enL+Pl9Im5SLMYUdfehW8iGPUxkDqV/35T4BMHRp0gFZjJud3l8vhwvCXQsCtRMj6zRocaGcbjzZ1lT3htXBhuXE4KTHodOrq80vMiHjw+PxqDzfco49PdovE5sBj1qGrtRFlV/yc92ZwuyvhEJzslCX+7Zi7Sk40A6DkIBLKNF84IHG2PdMBrf9R+eIECnzi5LJhq3PB9PWra5HkTqmx2QhQDc1qyUxLjU+KUglSY9Do0Odw41iz/m3t4Dx/eGfU6FOcFMn/x3O5ioyoMOgGZyYnxPIyUxaTH4omB7a7+Znd5fH6pPoVOdUVvXE4KXl41B+PzUnDp7BFKL4cL7MP3R3tq0eHyDOm+2GT2PJUG5RT4xMnYnBTMK8yEXwTe2SLP0Dh2lH1stjVhUrtJBr207RRJwehQtDrdqGoNBFeTVJDxAULbXSxgiwfpxIctCTpdYjwPoyENLS2r6TNLybZp9DoB6RZj3NamJXPGZGL9XYuwnAIfAMDMkekYm2OFy+Mf1MDccPUqbwxJgU8cXTF3JADg7S3HZJkvdVgaTpoY9T1MvAaWsqzJqMxkpJrV8WakxJF2tafB5Xb6xFyYDDpUNDmxv48eS2ybK9NqouCRxIQgCLh0ViAIHMrEdr9fDGV8VPo7ToFPHJ0zdRhsZgOOt3Tim0NDLzDriR1lT5T6HqYkbGCpnNS0zcVIR9pr4xf41FPX5n6lJBlwanEOgL63u1iNVKIPeCWxdVHJcAgCsOlIM441D26MUpPDDZ9fhCBAtSUVFPjEkcWkx8UlgX3Wt2Qocj4iZXwSK/CZFcz47Klul3U8g5pOdDEsSDvW3In2Ie7rRyrR5yNF4txpoe2u3rCj7DkqraEgfBqebsHCsVkAAkfbByPUtVm9A4jVuWoVuzy43bV+T620jx8rrMYn0TI+w9MtyLElwesXsXuAkzJDwba61JTxSU82oSA4p2h/TXxGV1APn4GdMSkPRr2Ag/V2lNfbT7i9yUHjKog82HbX2u1VgzoJq4WMLgU+cTalIA3ThqfB4xNjcqyQaXG40RI8BZJogY8gCLL383F5fDgYfIOaMlw9gQ8Q/zoftv+fS9mKPqVZjPjBuMAcv492n5j1oa0uIpezp+Yj2aTHkUYHtg3i9bJeAx9sKPBRwBXzAlmft0qPxaz3zJHgHLBhaWYkmxKvS6lU5yPTya6DdXb4/CIyko2qa3/PtubiFvjQVldEpO2uXup8qIcPkYs1ySCdLBzMCIs6lY+rADgOfDo7O3H55Zdj7dq13f7+yJEjuOqqq1BSUoKTTz4ZL7zwQrfbv/zyS1xwwQWYOXMmzjnnHGzYsCGey47IhTMKYDHqUV5vx7YYFeQmamEzM0vmk13SRPaCVNW1CpCOtMcp8KEan8gsnZwPvU7Anup2VDZ1LzSljA+RE9vu+s/O6qjrIqWuzSodVwFwGvgcPHgQV155JXbs2NHt7z0eD2688UZMmzYNmzZtwosvvojVq1fjww8/BABUVFTgtttuwx133IEtW7bgtttuw5133om6ujoFfoq+2cxGnD890Lr+zc2xKXIOjapIzMBn2og06HUCatpcsjSIZIXNUwrSYn7fcmOBz/e1HbLPiuvy+qQtVzV/IoyHTKsJ8wszAQAf9tjuojldRE4Lx2ahIM2MdpcXn+2LboySFjK63AU+GzduxFVXXYWLL74YBQUF3W4rLS1FfX09br/9dphMJkyePBkrV67E6tWrAQDvvfce5syZgyVLlsBgMODcc8/F3Llz8fbbbyvxo/SLbXe9v6smJqdtQhmfxOrhwySbDJiYHzi6LUfWZ48Kj7IzozOTkWzSo8vrR0VwS1QubP/fZNAhjRrvDeicqb1vd0nFzTSZnchApxNwUfCEcbS1plrY6op7MYjL5eozA5OTk4OJEydiw4YNSEpKwt/+9rdutx88eBCFhYUwmULp36KiIrz44osAgPLycowfP77b9xQVFWH//v1Rr9Pni82xaHY/Pe9vxvBUFOVYUd7gwL+3HceP5o8a0uOwE11jMi0xWztv+rqWzMwRadhT3Y6tR5tx1uTcmD2u3y9K9TET8qyqvL4T8m3YXtmKsuOtKMxKHvBaDlZNa2DLJs+WBL9f3uwSD4Z6HZdMzMED/wfsONaKY012FKRb4PeLaApudWUkG1T5fBsMuZ6TiSiSa3nRzGF47otD+OJAA+ranBGfIAwdZzdy928V6XriHvjs3LkTq1at6vW2Z599FkuWLOnzex0OBywWS7e/s1gscDqdfd5uNpul26NRVlYW9fdEe38nF+hQ3gC89vUBTE5qHvR9+0URR4KBj6vhKHZ0Dq4/g1r09W+TKQa2uL7ZV4Udw1wxe7wauxcOtw8mHdBRVY4dNeqq8QGAHEPgU9qXOw9hlBhKbcf6eb75eOC6W3XeE7aqtWwo13FilhH7Gj149ZNtOL/Yig63H95gZ/fjh/ahLsE6N8f6OZnIBrqWxZlGHGz24IUPtuD88QOXSfhEUdqGbawsx44GfUzWGW9xD3zmz5+P77//flDfm5ycjM7O7vUbnZ2dsFoD/2AWiwUuV/c3PJfLJd0ejWnTpkGvH/o/qs/nQ1lZWa/3N6rYjdW7N+BQixfG3LGDnvhd1dIJt78ORr2ApT+YDb1GXyj7u5YAkDbCgWdKv8aRNh8mT50OkyE2O7nVZbUAGjFxWBpmzyqJyX3G2yldlVh/eC+a/RbMnDlzwGs5WDucFQBaMXZYFmbOnBmz++VVLK7jpY4KPPLBfuxuNeD+mTODfX3qkWo2YK5Kn2+DIddzMhFFei1/7KrEg+v2YlM9cP9lMwe83/p2F/yog04ATp0/i7v3GvZzD0RV556Li4tRUVEBr9cLgyGw9PLychQXFwMAxo8fjz179nT7nvLyckydOjXqx9Lr9TH95evt/nJSLThzSj7e31WDNVurMH1kxqDuuyI4lXx0lhUmo6r+SQelr3+bcbk2pCcb0er04EC9AzOCvX2Gan9doPHflOGpqn1BnjIiHQCwr7aj288Q6+d5gz1Y2JxmVu21GoyhXMdzphfgkQ/2Y8vRFjQ5PWjp9AIIFDYn0jVkYv2cTGQDXcsLZw7HIx/sw96aDhyod0gHIfrS6Ag8N3NsSap+r+GuuLk/8+fPR0ZGBp544gl0dXVh//79+Mc//oHly5cDAC688EJs3rwZH3zwAbxeLz744ANs3rwZy5YtU3jlffvh3EBtz792VKHTPbj9UjaqIlGPsjPdGxnGrp+PGmd09TQx3wZBABo6uqQeMXLQwomPeBuebsGMkekQRWD9nrpQDx8qbCYyy7CacMbEPACRFTlrpVWFqgIfg8GAV199FQcOHMBJJ52EG264AStXrsQll1wCABg3bhyeffZZ/PWvf8XcuXPx3HPP4ZlnnkFhYaHCK+/bD8ZlYUSGBR0u7wlHWiOVqDO6ehNqZNgas/uUTnSp8Cg7k2wyYExW4PkhZyPDOg20s1dC6HRXTdhRdurhQ+R3yazA6a5/7agesN2FFnr4AJxvdX3++ecn/N3o0aPxyiuv9Pk9p5xyCk455RQ5lxVTOp2Ay+eMxBOfHMBbm4/hkmBjqWgcChY2j03wjA8AzJImtbfG5P4aOrpQ39EFQYB0XF6tJg9LxZFGB/bVtOMHYzNleQzpqKvKXxjj7Zyp+Xj8w/347nCzlLmlOV0kHk6bkItMqwkNHV34urwRp0/o+0Qs+/3OVfkHG1VlfLRqxZyR0AnA5opmKYiJRijjk5g9fMJNH5kGQQAqm50x2dJh2ZHCLCusSVx/ThjQpGGBwI1t3cmBpcJzVZ4Kj7fRWVZMHpYKn1/Ev7dXAwhMvyZEbiaDDhfOCPTMW7ut/xPB0la2yj/YUODDgfw0sxRlv10aXSdnl8eHqtZAcXOi1/gAQKrZiOLcQAAYi6yPNJF9kCfueBIaVirPlHan24sOV6D4kba6ose2uzq6WHEzbXWR+GAjLNbvqe23oW6oxkfdv98U+HDi8rmBTs7vbj0Otzfyxm9Hm5wQRSDVbKC5PkElI9l219ALnEP1PdoJfA412NEV5XyeSLCuzckmPVJUnh1TwjnBoaUMZXxIvEwdnori3BR0ef34YFfftab1HeqfzA5Q4MONxRNzkWtLQpPDjc/2RT5bjHVsLsxJUd3wTLmUxHBg6d7q4HBSFZ/oYoalmZGebITXL+JgffRbqgMJP/FBz8XoFeXaUJQb2q7OoYwPiRNBEHDp7EDWp7+J7VTjQ2LKoNdhefCJ92YU212Hg/U942ibSzJrdCDjs/N4K3zBDriD4XR7peurhYyPIAiYlB/4OfbXxn67qy74aTCXBmsO2rlTQ1kfKm4m8XTRzOHQCUBpRQuO9jLTz+PzSzPkKONDYoZtd319sAHHWyIbsxEaTkqBD1OUkwJbkgFOtw/fD+ENfn9tB0Qx0KxL7cc3GanOR4bAh3r4DN3ZU4dJ/z+LAh8SR/lpZpxUlA0AeG/7iUXOjfYuiCJg0AnITFZ3NpICH46MzrLiB+OyIIrAmi2RTcw90hg8yk4nuiQ6nSB1bd5+bPB1PlpoXNgTy1zJUeCslcJHJU0aZsPPlo7Hr86dSHVSJO5YkfPabVUQxe7ZcrbNlWNLgo6zURXRosCHMyzrs2bLsYi2aahrc+9iUeejpRNdDDvSvq+m/YQXtqGSevhQxmfQBEHAbWcU44ZTxym9FJKAzpqSD6tJj8pmJ7Yc7f6hUUutKijw4cxZU/KRnmxEdZsLXx1s6PdrWxxutDgDRw/HZCfHY3mqEQp8Bp/xYSe6Bjs8lkdFuSkw6AS0u7xo7Iz89GAktPTCSEgispj0OHdaYLv13a3ddx2kE10aqOGjwIczZqMeF5cEWoi/vbn/ImdWeFuQZkayidLi4diR9kMNDrQ5++5L0Revz4/9Ndrb6koy6KWTQxWt0V+X/mjphZGQRMVOd72/qwausLYXWqrho8CHQ1cEB5d+uq8ODR19dx8OHWWnba6eMqwmaftvMHU+FU0OdHn9SDbppRlXWsECuUMtsQt8RFHUzABDQhLZvDGZGJ5uQUeXF+v3hlqraKmGjwIfDk3It6FkVDq8frHfngrSqIpsKmzuTWhSe2vU38u2uSYNS1V9IV9PrGZpzV4HLvzfb/HUpweHXPNj7/LC6Q58OlR7jw9CEplOJ+DS4ODS8IntoR4+6v9gQ4EPp64IFjm/XXqszzckOsreP6nOZxCT2rV4oou5cEYBFo7NhA7Anpp2/OXTAzjnqa9x6h834OH/7MV3h5sGnNLcE3tRtJkNtO1KiMpdHDzd9dWBBmmLS6rh08BWNgU+nDp/egGsJj2ONDqw6Uhzr18TGk5KgU9vSoKT2ndUtsAfZSNDLZ7oYnJTzXjjJ/Pw8oW5+P0lU7FkUh6SDDoca+7EK/89gite/A5zH/0UP1+zE+v31KLTPfB4Cy3t/xOS6AqzrZg9OgN+Efj3jsDQXK2MqwAA+mjGKWuSARfOLMCbm4/h7dJjWDA2q9vtPr+II0201dWfifk2mI06tLsCHZjDxwH0RxRFTWd8mLQkHRbNHIHL542G0+3F1wcbsX5PHT7bX4cWpwfvbD2Od7Yeh9mowynFOThzch7OmJSHzF5mwtV1aGf/nxACXDJrOLYebcG7247jqh+MQbPDDYACHyKzy+eOwpubj+GDsho8dMEUpCUbpduqWzvh9vph0uswPMOi4Cr5ZdDrMH1EOjYfaca2ypaIA5+69i40OdzQ6wRMyLfJvEo+JJsMOGtKPs6akg+vz4/SihZ8srcO6/fW4nhLJz7ZW4dP9tZBJwBzx2Ri6eQ8nDUlHyMzA20UpB4+GulwTUiiO396AX67bi/213bgywOB1ipGvYCMsPchtaKtLo7NGJGGifk2dHn9+NeO7i3E2VH20VnJ0Gus+DaWBtPIcG9NYDDpuBwrzEa9DKvim0Gvw8JxWXjggsn4+pen44PbT8GdS4oxeVgq/CKw6UgzHnl/H075wwac/eRX+PMnB7AjeH21UPhICAHSLEYsnZwHAHjui3IAQK5NGwOIKePDMUEQcMXckXho3V68ubkSqxaOlp50RxrYqAqq7+kP6+cTTSPDRNjmipQgCJhckIrJBam4c8l4HGt24tN9dVi/pw6bK5qxv7aj28BT2uoiRDsunTUc7++qkT44auX3mzI+nLuoZDhMBh3213Zg1/E26e9Doyqovqc/s4IZnwN1HbB3eSP6nlDH5jS5lqVaIzOTcc1JhXjzhgXY8usleGLFDJw9JR+WYGZszuhMhVdICImVU4tzkJ0SqunTQn0PQIEP99KTTTh3aj4A4K3SUCfnw1IPH8r49Cc31Yzh6Rb4RWDX8daIvkfLJ7piKcNqwqWzR+CFlbOx/YGl2PabpZg2goJFQrTCoNdh2czh0p+1cJQdoMBHFS4PdnL+vx1VcASzFqyHD211DSyaOp8OlwdHm5wAaKsrGmajvtfTXoQQdWMT2wHt1PBR4KMCC8ZmYkxWMhxunzQ/pbqtEwA1L4wE6+cTSZ3PvppAvUpBmhkZ9EZOCElwkwtSMTF4unWERk4QU+CjAoIgSFmft0orUdHkgCgGqu7pU/bAZoVlfAYay7C3OlBHRdtchBAS8MwPS/CLsyZIk9vVjgIflbh09nDodQK2Vbbio921AALZHi0cLZTb5IJUmPQ6NDncONbc2e/X7tXgRHZCCBmK4jwbbjm9CEa9NkIGbfwUCSDXZsYZE3MBAC99dRgA1fdEKsmgx5ThgUBm2wDbXVTYTAgh2kaBj4r8cF5gu8sRnJ1EJ7oiF0k/H4/PjwO1gf5IdJSdEEK0iQIfFTl1fA7yw6rqx+ZQD59IRTKpvbzeDrfPD1uSQTNFfIQQQrqjwEdF9DoBl80JHS2kE12RmzU6kPHZW90Ol6f3aeOsY/OkglSqnSKEEI2iwEdlLps7EnqdgGSTHmOyKPCJVEGaGbm2JHj9InZXtfX6NaGOzVTfQwghWkWzulRmREYy/t9186HXCbCYEm+A5mAJgoCSUen4eE8dtlW2YM6YE0crsOGkdKKLEEK0izI+KjR/bFavb9ykf6FGhq0n3CaKYmg4KWV8CCFEsyjwIQmjZGQ6gN4Dn+MtnWh3eWHUCyjOtcV3YYQQQuKGAh+SMKaPSIdeJ6C23YWatu6NDFn/nuJcG0wG+rUghBCtold4kjAsJj0mDQtkc3pmfWibixBCEgMFPiShsEaG2452b2TITnRRYTMhhGgbBT4kofTVyHBfDR1lJ4SQRECBD0kos4Inu8qq2uD2+gEArU43qloDNT+TKPAhhBBNo8CHJJTRWcnISDbC7fVLWR5W2Dwy04JUs1HJ5RFCCJEZBT4koQQaGXYfWMoKm6cMo8GkhBCidRT4kITD+vlsC57sohNdhBCSOCjwIQlHyvgcC2Z8auhEFyGEJAoKfEjCmTEyDYIAHGvuRFVrJw7W2wFQxocQQhIBBT4k4djMRowPjqV4u/QYfH4RGclGDEszK7wyQgghcqPAhyQk1s/nrc2VAALZHkEQFFwRIYSQeKDAhyQkFvjUd3QBoPoeQghJFBT4kITEGhkyUwroKDshhCQCCnxIQhqXkwJbkkH6MxU2E0JIYqDAhyQknU7AzOB2V5JBh7HZVmUXRAghJC4o8CEJizUynJhvg0FPvwqEEJII6NWeJKxLZo1AcW4KfrxgtNJLIYQQEieGgb+EEG0ak23FJ3cvUnoZhBBC4ogyPoQQQghJGBT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEgYFPoQQQghJGBT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEgZ3gc/x48dx6623YsGCBZg/fz5uvvlmHDt2TLr9yJEjuOqqq1BSUoKTTz4ZL7zwQrfv//LLL3HBBRdg5syZOOecc7Bhw4Z4/wiEEEII4RR3gc8tt9yCtLQ0fP755/j888+Rnp6Om2++GQDg8Xhw4403Ytq0adi0aRNefPFFrF69Gh9++CEAoKKiArfddhvuuOMObNmyBbfddhvuvPNO1NXVKfkjEUIIIYQTXAU+bW1tyM7Oxh133IHk5GRYrVasWrUKBw4cQFtbG0pLS1FfX4/bb78dJpMJkydPxsqVK7F69WoAwHvvvYc5c+ZgyZIlMBgMOPfcczF37ly8/fbbCv9khBBCCOFB3Gd1uVyuPjMwOTk5eOWVV7r93ccff4zhw4cjLS0NBw8eRGFhIUwmk3R7UVERXnzxRQBAeXk5xo8f3+37i4qKsH///qjX6fP5ov6e/u4nVveXyOhaxg5dy9ig6xg7dC1jJ1GvZaQ/b9wDn507d2LVqlW93vbss89iyZIl0p/ffPNNvPrqq3j++ecBAA6HAxaLpdv3WCwWOJ3OPm83m83S7dEoKyuL+nvieX+JjK5l7NC1jA26jrFD1zJ26Fr2Lu6Bz/z58/H999/3+zVutxuPPfYYPvjgA/z1r3/FggULAADJycno7Ozs9rWdnZ2wWq0AAkGQy+XqdrvL5ZJuj8a0adOg1+uj/r6efD4fysrKYnZ/iYyuZezQtYwNuo6xQ9cydhL1WrKfeyBxD3wG0tzcjJtuuglutxvvvPMORo4cKd1WXFyMiooKeL1eGAyBpZeXl6O4uBgAMH78eOzZs6fb/ZWXl2Pq1KkRP74oijH4KQghhBCihIHexwWRo3d6j8eDyy+/HBkZGXj22WdhNpu73e71enHWWWfhzDPPxJ133okjR47g+uuvx1133YVLLrkEhw4dwsUXX4zHH38cZ555JtavX497770X//73v1FYWBjRGtxuN6UHCSGEEJWaNm1at1rgnrgKfNavX4/bbrsNSUlJJ6Tn3n//fRQUFODo0aP4n//5H+zcuRPJycn48Y9/jBtuuEH6uq+//hp/+tOfUFlZieHDh+MXv/gFFi1aFPEa/H4/vF4vdDodBEGI2c9GCCGEEPmIogi/3w+DwQCdru9D61wFPoQQQgghcuKqjw8hhBBCiJwo8CGEEEJIwqDAhxBCCCEJgwIfQgghhCQMCnwIIYQQkjAo8CGEEEJIwqDAhxBCCCEJgwKfKOzfvx/XXHMN5s2bh5NOOgm//OUv0dzcDCAwfHXFihUoKSnB4sWLsWbNml7v429/+xtWrlzZ52P85S9/weLFi2VZP0/kupaNjY2YMGECSkpKpP+0fj3lupZ+vx//+7//i0WLFqGkpAQrVqzA9u3bZf95lCLHdayuru72XCwpKcH06dMxYcIEupaDeE42Nzfjrrvuwvz58zF//nzcfPPNqK6ulv3nUZJc17KjowP3338/Fi5ciPnz5+O+++5De3u77D8PF0QSkc7OTvGkk04Sn3rqKbGrq0tsbm4Wr7/+evGnP/2p2NraKs6bN0984403RI/HI3777bdiSUmJuHPnTun7HQ6H+Nhjj4njx48Xf/zjH/f6GN9++604ZcoU8fTTT4/Xj6UIOa/l559/rvnrF07Oa/nMM8+IZ511lnj48GHR6/WKf/3rX8V58+aJXV1d8f4xZReP329RFEWPxyOuWrVK/NWvfhWPH0sRcl7LO+64Q7z77rtFh8MhOhwO8c477xRXrVoV7x8xbuS8lrfccou4fPlysbq6WrTb7eLdd9/d73NXSyjjE6Hq6mpMnDgRt9xyC0wmEzIyMnD55ZejtLQU69evR3p6Oq688koYDAYsXLgQF1xwAVavXi19/7Jly9DQ0IAf/vCHvd5/Y2Mj7r///n6zQVoh57UsKyuLaiit2sl1LX0+H/7+97/jN7/5DQoLC6HX6/GTn/wEL7/8crx/xLiQ+/ebef7559HU1IQHH3xQ7h9JMXJey0OHDkEURek/nU4Hi8USzx8vruS6lp2dnfjss8/wy1/+EsOGDYPVasV9992HzZs349ChQ/H+MeOOAp8IjR07Fi+//HK3GWIff/wxpkyZgoMHD2L8+PHdvr6oqAj79++X/vyPf/wDTzzxBLKysk64b7/fj5///Oe4/vrrpUnzWibntSwrK0NtbS3OP/98LFiwANdffz3Ky8vl+2EUJte1rKioQHt7O9rb23HJJZdg/vz5uO6665CUlNTv8D+1kvM5yVRWVuLFF1/EI488oslryMh5LW+66SZs2LABs2fPxuzZs7F79248/PDD8v0wCpPrWvr9foii2C1oZLOtDh8+LMePwhUKfAZBFEX85S9/wYYNG/DrX/8aDofjhE8dZrMZTqdT+nN+fn6f9/f888/DZrPhiiuukG3NvIr1tUxNTcXs2bPx+uuv49NPP8WYMWNwzTXXoKOjQ7afgRexvJatra0AAi+czzzzDL788ktMmTIFP/nJTzR/LWP9nGReeOEFLFq0CDNnzoz1krkV62vp9/tx+eWXY9OmTfj2228xduxY3HnnnXItnyuxvJZWqxUnnXQS/vKXv6ChoQF2ux1/+MMfoNfr4XK5ZP05eECBT5Tsdjtuv/12rFu3Dm+88QYmTJgAi8VywpPF5XLBarUOeH+lpaVYu3YtHnnkEbmWzK1YX0sAeOKJJ3DPPfcgMzMTKSkpuO++++BwOLBlyxY5fgRuxPpasozErbfeiuHDh8NsNuPuu++G3W7Htm3bZPkZeCDHcxIAHA4H3n//faxatSrWS+ZWrK9lQ0MD7r33XvzkJz9BWloaMjMz8dBDD2HLli34/vvv5foxuCDH8/KPf/wjMjIysGzZMlx66aWYOXMmbDYbUlNT5fgRuEKBTxQqKytx6aWXwm6345133sGECRMAAOPHj8fBgwe7fW15eXlE21b/93//h+bmZpxxxhmYM2cOfvvb36K6uhpz5szR9Ju1HNfSbrfj97//PaqqqqS/8/l88Hq9MJvNsf0BOCLHtSwsLITBYIDb7Zb+Lry2QovkuI7Ml19+iczMTMydOzema+aVHNeyoaEBHo+n23PSYDAAAIxGYwxXzxe5npcNDQ34zW9+g2+//RYff/wx5s6di7a2toSokaTAJ0JtbW246qqrMGvWLLzyyivIzMyUblu6dCkaGxvx2muvwePx4LvvvsO6detw6aWXDni/Dz/8MLZv344tW7Zgy5YtePDBB1FQUIAtW7Zgzpw5cv5IipHrWqakpODbb7/F73//e3R0dMDhcODhhx/GiBEj6FoO4lqef/75eOyxx3D8+HG43W786U9/QmpqKhYsWCDnj6QIua4js23bNsyePRuCIMixfK7IdS2LioowcuRIPProo7Db7bDb7fjd736H6dOnY8yYMTL+RMqR83n5xz/+EY8//jjcbjfq6urw29/+Fuedd16/dWpaYVB6AWqxdu1aVFdX48MPP8RHH33U7bbt27fj1VdfxaOPPoqnn34amZmZuP/++zX5BhELcl7L5557Do899hiWLFkCj8eD+fPn46WXXtLsJ0I5r+XDDz+MZ555BqtWrUJLSwumTp2KV155RZPZM7l/v48dO4aioqJYL5tLcl1Lk8mEV155Bb///e+xZMkS6HQ6LFiwAM8++6xUmKs1cj4vH3nkETzwwAP4wQ9+AKPRiHPOOQe//OUv5fgxuCOIWs1bE0IIIYT0oM0wmRBCCCGkFxT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEgYFPoQQQghJGBT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEgYFPoSQqF177bW49dZbe73tn//8J37wgx90m6kUSyUlJTGZY7d27VosXrwYALBlyxaUlJRIt1VXV6OkpATV1dV44YUXcN111w358QghfKCRFYSQqK1cuRK33norGhoakJOT0+22N998E1dccYU04T3Wtm/fHvP7nDNnTrf7LSgokP584403xvzxCCHKoYwPISRqixYtQkFBAd57771uf79jxw4cPHgQSUlJUjaFWblyJZ555hkAgN/vx4svvoglS5Zg9uzZWL58Ob7++mvpaz/++GOcd955mD17Ns455xw899xz0m0TJkzApk2bAACLFy/GX//6V1x00UUoKSnBRRddhO+++0762srKStx4442YP38+Tj/9dPzlL3/pNRO1adMmaeo1AHz++ee44oorsHDhQsyYMQM//vGPUVFRASCQKfrhD3+IRx55BAsWLMDChQvx61//Gh6PBwBw77334oEHHsCNN96IkpISnHHGGXj99del+7bb7fif//kfLFq0CAsXLsRdd92FxsZG6fZnnnkGixYtwrx583DppZfis88+i+wfhRASEQp8CCFR0+l0+NGPfoQ1a9YgfNzfm2++ibPPPvuELFBPzz77LFavXo2nnnoKmzZtwrXXXoubb74Zu3btgsvlwi9+8Qs88MAD2Lp1K5544gm89NJL2LVrV6/39e677+Kpp57Ct99+i4kTJ+Khhx4CADidTlx99dUoLi7GV199hf/3//4fvv32Wyn46kttbS3uuOMO3HDDDdi4cSO++OILiKKIZ599Vvqabdu2ISsrC19//TX++te/4oMPPsD69eul29euXYuVK1eitLQU119/PR5//HHU1dUBAH71q1/h6NGjWLt2LT799FOkpKTg1ltvhSiK+O677/D2229jzZo12LRpE1asWNEtqCKEDB0FPoSQQVm+fDkaGxulDEtrays+/PBDrFq1asDvfffdd3HDDTdgypQpMBgMOPfcc7F48WK88847AACz2Yx33nkHGzduxLhx47B161ZMnz69z3WMHj0aFosFF1xwgZSZ+eKLL+B2u3H33XcjKSkJw4YNwx133IHVq1f3u7bMzEy8//77WLx4Mex2O2pra5GRkSEFLmx9N954I4xGI6ZPn44JEybgyJEj0u3z58/HSSedBIPBgEsvvRQ+nw+VlZVoamrCxx9/jF//+tfIysqC1WrFr371K5SVlWHPnj1ISkpCW1sb/vnPf2Lv3r1YsWIFNm7cCKPROOA1JYREhmp8CCGDYrPZcOGFF2LNmjVYuHAh3n33XUyePBnTp09HeXl5v9/b2NiIkSNHdvu7ESNGYP/+/TCbzXjzzTfx3HPP4Wc/+xnsdjvOOuss3H///UhLSzvhvrKzs6X/bzAYpAxUVVUVmpubMXfuXOl2URTh8XjQ1NTU59qMRiP+85//4K233oIgCBg/fjzsdjsMhtDLZVZWFgRB6PY94Zmv8IwXC1r8fj+qqqoAAJdddlm3x9Tr9Th+/DjOPvtsPPPMM/jHP/6Bl19+GWazGStXrsRNN90EnY4+pxISCxT4EEIGbeXKlbj44ovR0tKCf/7zn7j99tsBBLbCetbStLS0SP9/+PDhOHbsWLfbjx07htzcXNjtdtTX1+OJJ54AAOzbtw933303XnjhBdxzzz0Rry0/Px+jRo3CRx99JP2d3W5HU1MTMjMz+/y+Dz/8EG+88QbefPNNjB49GgDw8MMP48CBAxE/dl/y8vKkxwgPjsrLyzFy5EhUV1cjKysLr7zyCtxuNzZu3Ihbb70VU6ZMwWmnnTbkxyeE0FYXIWQIioqKMHv2bDz++OPo7OzEmWeeCQAYN26ctA0miiL+/e9/49ChQ9L3rVixAi+++CL27NkDn8+HDz/8EJ9//jkuvvhiOBwOXH/99Vi3bh1EUURubi50Oh0yMjKiWtvpp58Oh8OBl19+GW63G+3t7bjnnntw1113dcvW9NTR0QGdTgez2QxRFPHVV1/hX//6V0zqbPLy8nDaaafh0UcfRUtLCzweD55//nksX74c7e3tKCsrw3XXXYf9+/fDZDIhKysLAKL+2QkhfaOMDyFkSH784x/jlltuwZ133ilt60ybNg033XQT7r33XjgcDixZsgRnnXWW9D3XXHMN/H4/7rrrLjQ0NGD06NH485//jHnz5gEAnn76aTz55JN44IEHYDabce655+Lqq6+Oal0pKSl47bXX8Pjjj+Pll1+G3+/H/Pnz8fzzz/f7fRdffDG2bt2K888/HzqdDoWFhbj22mvx+uuvx6Q30R/+8Ac88cQTuOiii2C321FcXIyXX34ZOTk5OOuss1BRUYGbbroJLS0tyMrKwq9+9SvMmDFjyI9LCAkQxPCNaUIISUAbN27Etddei3379im9FEKIzGirixCS0FwuFw4ePChtKxFCtI22ugghCe3Xv/41vvrqK9x5551KL4UQEge01UUIIYSQhEFbXYQQQghJGBT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEgYFPoQQQghJGBT4EEIIISRhUOBDCCGEkIRBgQ8hhBBCEsb/B/po8CDFanIAAAAAAElFTkSuQmCC",
      "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",
    "# Tässä ennustevirheet eivät vaihtele satunnaisesti, vaan niissä näkyy säännöllinen kausivaihtelu\n",
    "df['Ennustevirhe'].plot()\n",
    "plt.ylabel('Ennustevirhe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Toteutunut kysyntä')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u3SpJcjqdmjlz5nnzR44c8eUwJElut9vnx3Skpv2bXYc/ojfe0RvvvPXmmKtGRY7y87c3DBU5yuU8+aWGRYV1SY1mYd14R2+887fetLYOn4LN1KlTtX///n88ODhYPXr00KxZszzGW1JVVaUPP/xQo0eP1vbt21VXV6d7771X9913n6KiohQaGuqxfe/evVVbWytJqqmpaXHeF2VlZT4/pjP4Sx3+iN54R2+8+3Zv9n9+zsuWX9tT+oGqBvXqzJL8BuvGO3rjXaD15qLB5sSJE7r99ttlGIbOnj2rKVOmeMzX1dVpyJAhrd5hz549JUkPPvigevXqpb59++qnP/2pbrnlFqWlpamuru68rx8W9vX/pkJDQy84HxER0er9N4mNjZXNZvP5cR3F7XarrKzM9Dr8Eb3xjt545603/S+pUfZbb3p93OQJo7vFGRvWzYXRG+/8rTdN9VzMRYPNpZdeqgcffFCnTp3SihUrtGjRIo/5Xr16acKECa0uzG63q7GxUQ0NDerV6+v/JTW9i/EPf/hD/fd//7fH9k6nUzExMZKkmJgYORyO8+YTExNbvf8mNpvNL54of6nDH9Eb7+iNd9/ujX1gPyXGRKvY6ZLbMP6xXVCQEuxRsg/sZ0aZpmDdeEdvvAu03rTqUtTkyZMlSZdccokmTpzYrh1effXV+v73v6+lS5fq0Ucf1blz5/T444/r+uuv10033aQ1a9YoNzdXs2fP1nvvvafCwkKtW7dOkpSenq6FCxfqxhtv1Lhx45SXl6eKigre9RhAi9Zmxunu/AMe99ok2KO0NjPOxKoAdAaf7rEZN26cdu7cqePHjzefZWny7TM53oSEhOjZZ5/VqlWrNHXqVJ07d05JSUl68MEH1a9fP23atEnZ2dlas2aNIiMjtWzZsuZ3No6Pj9fy5cu1YsUKnTx5Una7XTk5OT6/3BxA99K/T4g23z5Rx1w1Ol5Ro6EDwix/+QnornwKNsuXL9fLL7+sUaNGKTj4Hw8NCgryaacDBw7U448/fsG52NhYbdmyxetjU1NTlZqa6tP+AECShkURaACr8ynY/M///I82b96s2NjYzqoHAACgzVr1Bn1NDMPQZZdd1lm1AAAAtItPweamm27S008/3Vm1AAAAtItPl6IOHz6s/fv3a/369YqMjPSYe/311zu0MAAAAF/5FGwyMjKUkZHRWbUAAAC0i0/BxjAM3Xjjjed9rAEAAIA/8Okem2eeeUYJCQlaunSp3nvvvc6qCQAAoE18CjaFhYXavHmz+vTpo0WLFmnq1KnasGGDTp482Vn1AQAAtJpPwUaSRo8erWXLlunNN9/UAw88oF27dikpKUnz58/XG2+80QklAgAAtI5P99g0OXz4sHbs2KGdO3eqsbFRc+bM0ZAhQ7Ry5Uq98cYbWrFiRQeXCQAAcHE+BZunnnpKO3bs0IkTJ3TNNddoxYoVuu6665o/XuGKK67QrbfeSrABAACm8CnYvPjii0pLS9OMGTMUFRV13vzgwYO1dOnSDisOAADAFz4Fm507d3r8+6OPPlLfvn01cOBASVJUVBTvcwMAAEzj083D+/fv14wZMyRJW7Zs0bRp0zRlyhS99tprnVEbAACAT3w6Y7N69Wpdd911MgxDGzZs0KpVqxQeHq7Vq1fr+uuv76waYRFHy6t1orJWQweEaVhUmNnltJvVjscKPjvzlU5/WK7h3/0OzwnQTfkUbI4eParnnntOR48elcvl0o9+9CP17NlTP/vZzzqrPljA6dp6Lc4/qCJHefNYYky01mbGqX+fEBMraxurHY8VnK6t1935B/SmwyXJJYnnBOiufLoUZbPZVFNTo6KiIo0dO1Y9e/bUp59+qr59+3ZWfbCAxfkHVex0eYwVO126O/+ASRW1j9WOxwoW5x/U284KjzGeE6B78inYXH/99frJT36idevWKT09XU6nU7fddptuuummzqoPAe5oebWKHOVyG4bHuNswVOQo1zFXjUmVtY3VjscKeE4AfJNPl6Ieeugh7dixQ71799aPfvQjHT9+XD/+8Y916623dlZ9CHAnKmtbnD9eURNQ90JY7XisgOcEwDf5FGzWrVunm2++WZdccokkaejQoZo3b16nFAZruDSyT4vzQwcE1i8cqx2PFfCcAPgmny5FffDBB7rxxht166236o9//KPOnTvXWXXBIoZH91ViTLRsQUEe47agICXGRAfc/6StdjxWwHMC4Jt8CjYbNmzQnj17dN111+npp59WQkKCfvnLX+r999/vrPpgAWsz45Rg93yn6gR7lNZmxplUUftY7XisYG1mnK62D/AY4zkBuiefPwQzKipK8+bN07x583Tw4EH96le/UkFBgYYPH65/+Zd/0axZs5o/OwqQpP59QrT59ok65qrR8YqagH/fF6sdjxX07xOi3LnjtfPNferz3Ut5HxugG/M5gTQ0NGjPnj3asWOHioqKZLfbtXTpUg0ZMkTr16/XO++8o9/97nedUSsC3LAoawUAqx2PFQz+TrDGjoyWzWYzuxQAJvEp2Pzyl7/Url27JEkpKSn6wx/+oB/+8IfN84MGDVJmZmbHVggAANBKPgWbzz77TCtWrNCUKVPUs2fP8+YHDRrE2RoAAGAan24efuCBB3TjjTd6hJqvvvpKjz/+uCQpPDxc11xzTcdWCAAA0Eo+BZvbbrtNn376afO/HQ6H0tPT9eKLL3Z0XQAAAD7zKdhkZGRo7ty5OnnypHJycjRz5kyNGjVKL730UmfVBwAA0Go+3WOzaNEiud1u3XDDDQoPD9eaNWt03XXXdVJpAAAAvmlVsPnss8+a/56RkaFPP/1UTqdTw4cPb54bPHhw51QIAADQSq0KNklJSQr6xtuVG//3KbpTp06VYRgKCgrSX//6186pEAAAoJVaFWxef/31zq4DAACg3VoVbIYMGdLZdQAAALSbT6+KAgAA8GcEGwAAYBkEGwAAYBk+BZsZM2ZccDwpKakjagEAAGiXi948/PHHH2v9+vWSJKfTqQceeMBjvrq6WnV1dT7veOfOnbrnnnvUq1ev5rHrr79ejz32mA4dOqSVK1fK6XQqIiJCCxYsUEZGRvN227dv17p161ReXq7hw4froYceUlxcnM81APAPR8urdaKyVkMHhGlYVJjZ5QBoI3/4Xr5osPl//+//KSIiQqdOnbrgfGRkZPOHYPqirKxMqampevTRRz3Gq6qqNH/+fC1evFizZs1SaWmpFi5cqJEjR2rMmDHau3evsrKylJOTozFjxigvL08LFizQnj17FBoa6nMdAMxzurZei/MPqshR3jyWGBOttZlx6t8nxMTKAPjCn76XW/Vy73vvvVeS9P3vf1933XVXh+y4rKxMN95443nju3fvVnh4uGbPni1Jio+PV0pKivLy8jRmzBgVFBRo2rRpGjdunCRp7ty5ev7557Vz507NnDmzQ2oD0DUW5x9UsdPlMVbsdOnu/APafPtEk6oC4Ct/+l726bOiJkyYoNLSUq9zrdXY2KjDhw8rNDRUGzdulNvt1rXXXqt77rlHDodDI0aM8Njebrdr69atkr6+HPbtAGO323XkyBFfDkVut9un7Tta0/7NrsMf0RvvrNSbY64aj//dNXEbhooc5XKe/NKnU9lW6k1Hozfe0RvvWtubjv5evlg9F+NTsJkzZ855Yz169NCgQYN8enfiyspKXXbZZZo6darWrFmjU6dO6b777tOSJUsUHR193iWl3r17q7a2VpJUU1PT4nxrlZWV+bR9Z/GXOvwRvfHOCr3Z//m5Fuf3lH6gqkG9WtzmQqzQm85Cb7yjN95drDed9b3cVj4Fm2+fFamsrNTvf/97n9+ZOCoqSnl5ec3/Dg0N1ZIlS3TLLbcoLS3tvJuR6+rqFBYW1rztheYjIiJ8qiE2NlY2m82nx3Qkt9utsrIy0+vwR/TGOyv1pv8lNcp+602v85MnjPb5jI1VetPR6I139Ma71vamo7+XL1bPxfgUbL4tMjJSS5Ys0dSpU3Xbbbe1+nFHjhzRSy+9pF/84hfNH65ZX1+vHj16aMyYMfqv//ovj+2dTqdiYmIkSTExMXI4HOfNJyYm+lS7zWbzi0XsL3X4I3rjnRV6Yx/YT4kx0Sp2uuT+vw/WlSRbUJAS7FGyD+zXpq9rhd50FnrjHb3x7mK96azv5bZq9xv0VVVV6dy5lk9DfVt4eLjy8vK0ceNGffXVV/rss8/02GOP6eabb9bUqVPlcrmUm5urhoYGlZSUqLCwsPm+mvT0dBUWFqqkpEQNDQ3Kzc1VRUWFkpOT23soALrY2sw4JdijPMYS7FFam8nbNwCBxJ++l306Y/Pt97BpaGjQe++9p6uvvtqnnX7ve9/Thg0b9Nvf/lbr169Xr169NG3aNC1ZskS9evXSpk2blJ2drTVr1igyMlLLli3TpEmTJH39Kqnly5drxYoVOnnypOx2u3JychQeHu5TDQDM179PiDbfPlHHXDU6XlHD+9gAAcqfvpfbdSmqV69emjNnjmbNmuXzYydOnKgtW7ZccC42NtbrnCSlpqYqNTXV530C8E/Dogg0gBX4w/eyT8Hm22+mBwAA4E98CjZut1u7du3S8ePH1djY6DG3aNGiDi0MAADAVz4Fm+XLl+vll1/WqFGjFBz8j4c2vbIJAADATD4Fmz179mjz5s2KjY3trHoAAADazKeXezc2Nuqyyy7rrFoAAADaxadgc9NNN+npp5/urFoAAADaxadLUYcPH9b+/fu1fv16RUZGesz58llRAAAAncGnYJORkaGMjIzOqgUAAKBdfAo2N998c2fVAQAA0G4+BZs5c+Z4fWn35s2bO6QgAACAtvIp2Fx11VUe/z516pReffXVNn2kAgAAQEfzKdhc6N2F09LS9Jvf/KbDCgIAAGgrn17ufSGXX365Pvjgg46oBQAAoF18OmPz2Wefefy7oaFBL7/8sgYNGtShRQEAALSFT8EmKSnJ4+ZhwzDUv39/ZWVldXhhAMxxtLxaJyprNXRAmIZFhZldDgD4xKdg8+034bPZbBowYIBCQkI6tCgAXe90bb0W5x9UkaO8eSwxJlprM+PUvw/f4wACg0/32KxcuVJDhgxp/vO9731PISEh+slPftJZ9QHoIovzD6rY6fIYK3a6dHf+AZMqAgDfXfSMzSeffKIXX3xRkvTWW2/pd7/7ncd8dXW1Pvzww04pDkDXOFpe7XGmponbMFTkKNcxVw2XpQAEhIsGm8GDB8vhcKiyslJut1t79+71mO/Vq5eWL1/eaQUC6HwnKmtbnD9eQbABEBguGmx69OihJ598UpK0bNkyrVy5stOLAtC1Lo3s0+L80AGEGgCBwaebh++6667zXvLdZPDgwR1SEICuNzy6rxJjolXsdMltGM3jtqAgJdijOFsDIGC06eXexv/94PvmS7//+te/dmxlALrU2sw43Z1/wONemwR7lNZmxplYFQD4pl0v966srNTGjRs1ZcqUDi0KQNfr3ydEm2+fqGOuGh2vqOF9bAAEJJ+CzZAhQ87798qVK3XzzTdr+vTpHVoYAHMMiyLQAAhc7f6sKEn68ssvO+LLAAAAtItPZ2y+/R42DQ0NevPNNzV27NiOrAkAAKBNfAo2334PG5vNpri4ON15550dWhQAAEBb+BRsnn322c6qAwAAoN18vsfmo48+0sqVK7Vo0SKdOnVKzz33XGfUBQAA4DOfgk1xcbEyMjJ06tQpvf3226qrq9Pvf/97PfXUU51VHwAAQKv5FGx++9vf6vHHH9fq1atls9k0aNAgPfXUU3r++ec7qz4AAIBW8ynYnDhxQomJiZL+8a7DsbGxqqqq6vjKAAAAfORTsBk8eLD279/vMVZWVqZBgwZ1aFEAAABt4dOrou68804tWLBAmZmZamhoUE5Ojp599ln9/Oc/76z6AAAAWs2nYDNt2jT17dtXeXl5Gjx4sEpKSvTggw9q6tSpnVUfAABAq/kUbF555RXdeOONuvbaaz3Gn3/+ec2aNatDCwMAAPDVRYPN2bNnderUKUnS0qVLNXbsWBmG0Tx/5swZrVq1imADAABMd9FgU11drWnTpqmurk6SlJSU5BFsgoKCdP3113dehQAAAK100WATHR2t1157TWfPnlVKSopeeuklj/levXopKiqqTTt3u92aO3euhgwZolWrVkmSDh06pJUrV8rpdCoiIkILFixQRkZG82O2b9+udevWqby8XMOHD9dDDz2kuLi4Nu2/Ix0tr9aJyloNHRCmYVFhZpeDboA1h4tpWiPfD+9tdikwUXf7WdGqe2wGDBggSXrvvffUo0cPVVRU6NNPP1V0dHSbQ4309aeF79u3T0OGDJEkVVVVaf78+Vq8eLFmzZql0tJSLVy4UCNHjtSYMWO0d+9eZWVlKScnR2PGjFFeXp4WLFigPXv2KDQ0tM11tMfp2notzj+oIkd581hiTLTWZsapf58QU2qCtZ2pb9Tc3H160+FqHmPN4Zsu9HNp7MCe2jSyQZF9bSZWhq7UXX8/+fQ+NrW1tbrrrruUkJCgW265RUlJSbrtttv05Zdf+rzjd955R7t379YNN9zQPLZ7926Fh4dr9uzZCg4OVnx8vFJSUpSXlydJKigo0LRp0zRu3DiFhIRo7ty5ioiI0M6dO33ef0dZnH9QxU6Xx1ix06W78w+YVBGs7omS03rbWeExxprDN13o59L7X9Tr/3v+kEkVwQzd9feTT6+KWr16tWpra/Xyyy/rkksu0YkTJ/TII4/oscceU1ZWVqu/TkVFhR588EGtW7dOubm5zeMOh0MjRozw2NZut2vr1q2SJKfTqZkzZ543f+TIEV8OQ9LXl8Ha65irxiMJN39tw1CRo1zOk196Pe3XtP+OqMNq6I13zpNf6uDJ+vPGW7PmrI518zVvP5caDelNh6tbr5ELseq6ac/vp+Zt/aw3ra3Dp2CzZ88ebdu2rfnS1IgRI/TYY49p+vTprQ42jY2NWrJkiebNm6dRo0Z5zNXU1Jx3Sal3796qra1t1bwvysrKfH7Mt+3//FyL83tKP1DVoF6dXodV0ZvzdcSas7ruvm5YI21jtXXTkesg0HrjU7A5e/asvvOd73iM9evXT42Nja3+Ghs2bFDPnj01Z86c8+ZCQ0N15swZj7G6ujqFhYU1zze9Ouub8xEREa3ef5PY2FjZbO271tz/khplv/Wm1/nJE0a3eMamrKysQ+qwGnrjXd9BXyr7rbe9zre05qyOdfO19vxc6o6sum46Yh34W2+a6rkYn4LNFVdcoSeffFL33HOPgoKCZBiGnnzyScXGxrb6a+zYsUNffPGFxo8fL0nNQeW1117Tvffeq+LiYo/tnU6nYmJiJEkxMTFyOBznzTd9MKcvbDZbu58o+8B+SoyJVrHTJfc3XgJvCwpSgj1K9oH9uqQOq6I357MP7KexA3uq7IuGNq85q+vu68bbz6UeQWKNtMBq66Yjfj81PybAetOqm4ffe+89SdIvfvELbd26VYmJifrxj3+sxMREvfTSS3rggQdavcNXX31V+/fv1759+7Rv3z7ddNNNuummm7Rv3z4lJyfL5XIpNzdXDQ0NKikpUWFhYfN9Nenp6SosLFRJSYkaGhqUm5uriooKJScnt+HQO8bazDgl2D1fGZZgj9LaTPNfgg5r+tmkcF1tH+AxxprDN13o59KY7/bUk7OuMKkimKG7/n5q1RmbO+64Q/v379fIkSO1a9cuvf7666qoqNCQIUN07bXXqm/fvh1STEREhDZt2qTs7GytWbNGkZGRWrZsmSZNmiRJio+P1/Lly7VixQqdPHlSdrtdOTk5Cg8P75D9t0X/PiHafPtEHXPV6HhFTbd5nwCYp2/PHsqde6U+PlXHmsMFffvn0vfDe6vqE4f6h1r3Jb44X3f9/dSqYPPNdxoODw8/75VJ7dH0xnxNYmNjtWXLFq/bp6amKjU1tcP231GGRXWPBQP/wZrDxTStEbfbrYOfmF0NzNLdfla06lJUUFBQZ9cBAADQbq06Y3P27FlNmTKlxW1ef/31DikIAACgrVoVbEJCQrRo0aLOrgUAAKBdWhVsgoODdfPNN3d2LQAAAO3SqntsvnnzMAAAgL9qVbCZPn16Z9cBAADQbq0KNg8//HBn1wEAANBurQo2AAAAgYBgAwAALINgAwAALINgAwAALINgAwAALINgAwAALINgAwAALKNVH6kABLqj5dU6UVmroQPCNCwqzOxyLKWrehsoz2Fb6gyUYwMCAcEGlna6tl6L8w+qyFHePJYYE621mXHq3yfExMoCX1f1NlCew7bUGSjHBgQSLkXB0hbnH1Sx0+UxVux06e78AyZVZB1d1dtAeQ7bUmegHBsQSAg2sKyj5dUqcpTL/a0PcXUbhooc5TrmqjGpssDXVb0NlOewLXUGyrEBgYZgA8s6UVnb4vzxCn5xtFVX9TZQnsO21BkoxwYEGoINLOvSyD4tzg8dwE2abdVVvQ2U57AtdQbKsQGBhmADyxoe3VeJMdGyBQV5jNuCgpQYE82rT9qhq3obKM9hW+oMlGMDAg3BBpa2NjNOCfYoj7EEe5TWZsaZVJF1dFVvA+U5bEudgXJsQCDh5d6wtP59QrT59ok65qrR8Yoa3iekA3VVbwPlOWxLnYFybEAgIdigWxgWxS+MztJVvQ2U57AtdQbKsQGBgEtRAADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMgg2AADAMkwLNu+8844yMjJ05ZVXKiEhQVlZWaqrq5MkHTp0SBkZGYqLi1NSUpIKCgo8Hrt9+3YlJydr7NixSktL04EDB8w4BFjQ0fJq7fnwCx1z1ZhdCgCgDUwJNpWVlbrzzjuVmZmpffv2afv27Xr33Xf11FNPqaqqSvPnz9eMGTNUWlqq7OxsPfroo3r//fclSXv37lVWVpZWrVql0tJSTZ8+XQsWLNDZs2fNOBRYxOnaev3r0+8qafWfNe+ZUk3+jzf0r0+/q6raBrNLAwD4wJRgExkZqbfffltpaWkKCgrS6dOnde7cOUVGRmr37t0KDw/X7NmzFRwcrPj4eKWkpCgvL0+SVFBQoGnTpmncuHEKCQnR3LlzFRERoZ07d5pxKLCIxfkHVex0eYwVO126O5+zgQAQSILN2nHfvn0lSddee61Onjyp8ePHKy0tTU888YRGjBjhsa3dbtfWrVslSU6nUzNnzjxv/siRIz7t3+12t6P69mvav9l1+KOu7s0xV42KHOXn12EYKnKUy3nySw2LCuuSWi6GdeMdvfGO3nhHb7zzt960tg7Tgk2T3bt3q6qqSvfcc48WL16sgQMHKjQ01GOb3r17q7a2VpJUU1PT4nxrlZWVta/wDuIvdfijrurN/s/PtTi/p/QDVQ3q1SW1tBbrxjt64x298Y7eeBdovTE92PTu3Vu9e/fWkiVLlJGRoTlz5ujMmTMe29TV1Sks7Ov/MYeGhjbfZPzN+YiICJ/2GxsbK5vN1r7i28HtdqusrMz0OvxRV/em/yU1yn7rTa/zkyeM9qszNqybC6M33tEb7+iNd/7Wm6Z6LsaUYLN//34tXbpUf/zjH9WzZ09JUn19vUJCQmS321VcXOyxvdPpVExMjCQpJiZGDofjvPnExESfarDZbH7xRPlLHf6oq3pjH9hPiTHRKna65DaMf+w/KEgJ9ijZB/br9Bp8xbrxjt54R2+8ozfeBVpvTLl5eOTIkaqrq9Pq1atVX1+vTz/9VL/+9a+Vnp6uqVOnyuVyKTc3Vw0NDSopKVFhYWHzfTXp6ekqLCxUSUmJGhoalJubq4qKCiUnJ5txKLCItZlxSrBHeYwl2KO0NjPOpIoAAG1hyhmbsLAwbdy4UY888ogSEhL0ne98RykpKVq4cKF69uypTZs2KTs7W2vWrFFkZKSWLVumSZMmSZLi4+O1fPlyrVixQidPnpTdbldOTo7Cw8PNOBRYRP8+Idp8+0Qdc9XoeEWNhg4I85vLTwCA1jPtHhu73a5NmzZdcC42NlZbtmzx+tjU1FSlpqZ2VmnoxoZFEWgAIJDxkQoAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAPBwtr9aeD7/QMVeN2aX45JirRvs/PxdwdQPoWMFmFwDAP5yurdfi/IMqcpQ3jyXGRGttZpz69wkxsbKWfbvu7LfeDIi6AXQOztgAkCQtzj+oYqfLY6zY6dLd+QdMqqh1ArVuAJ2DYANAR8urVeQol9swPMbdhqEiR7nfXt4J1LoBdB6CDQCdqKxtcf54hX8GhECtG0DnIdgA0KWRfVqcHzogrIsq8U2g1g2g8xBsAGh4dF8lxkTLFhTkMW4LClJiTLSGRflnQAjUugF0HoINAEnS2sw4JdijPMYS7FFamxlnUkWtE6h1A+gcvNwbgCSpf58Qbb59oo65anS8okZDB4QFxBmPprqdJ7/UntIPNHnCaNkH9jO7LAAmIdgA8DAsKjACzbcNiwpT1aBeAVk7gI7DpSgAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZBBsAAGAZpgSbI0eOaN68eZo4caISEhJ07733qrKyUpJ06NAhZWRkKC4uTklJSSooKPB47Pbt25WcnKyxY8cqLS1NBw4cMOMQ0M0cLa/Wng+/0DFXjdmlBAT6BcAswV29w7q6Ov3bv/2bbrnlFm3YsEE1NTW67777tHTpUv3617/W/PnztXjxYs2aNUulpaVauHChRo4cqTFjxmjv3r3KyspSTk6OxowZo7y8PC1YsEB79uxRaGhoVx8KuoHTtfVanH9QRY7y5rHEmGitzYxT/z4hJlbmn+gXALN1+Rmbzz77TKNGjdLChQvVs2dPRURENIeY3bt3Kzw8XLNnz1ZwcLDi4+OVkpKivLw8SVJBQYGmTZumcePGKSQkRHPnzlVERIR27tzZ1YeBbmJx/kEVO10eY8VOl+7O50zhhdAvAGbr8jM2w4cP18aNGz3Gdu3apcsvv1wOh0MjRozwmLPb7dq6daskyel0aubMmefNHzlyxOc63G63z4/pSE37N7sOf+QvvTnmqvE489DEbRgqcpTLefJLDYsK69Ka/KU3F2J2v/y5N2ajN97RG+/8rTetraPLg803GYahJ554Qnv27NFzzz2nzZs3n3dJqXfv3qqtrZUk1dTUtDjvi7KysrYX3oH8pQ5/ZHZv9n9+rsX5PaUfqGpQry6qxpPZvbkQf+mXP/bGX9Ab7+iNd4HWG9OCTXV1tR544AEdPnxYzz33nEaOHKnQ0FCdOXPGY7u6ujqFhX39v7zQ0FDV1dWdNx8REeHz/mNjY2Wz2dp+AO3kdrtVVlZmeh3+yF960/+SGmW/9abX+ckTRptyxsYfenMhZvfLn3tjNnrjHb3xzt9601TPxZgSbD7++GPdcccdGjx4sLZu3arIyEhJ0ogRI1RcXOyxrdPpVExMjCQpJiZGDofjvPnExESfa7DZbH7xRPlLHf7I7N7YB/ZTYky0ip0uuQ3jH3UFBSnBHiX7wH6m1WZ2by7EX/rlj73xF/TGO3rjXaD1pstvHq6qqtKtt96qK6+8Uk8//XRzqJGk5ORkuVwu5ebmqqGhQSUlJSosLGy+ryY9PV2FhYUqKSlRQ0ODcnNzVVFRoeTk5K4+DHQTazPjlGCP8hhLsEdpbWacSRX5N/oFwGxdfsbmhRde0GeffaZXXnlFr776qsfcgQMHtGnTJmVnZ2vNmjWKjIzUsmXLNGnSJElSfHy8li9frhUrVujkyZOy2+3KyclReHh4Vx8Guon+fUK0+faJOuaq0fGKGg0dENbll58CCf0CYLYuDzbz5s3TvHnzvM7HxsZqy5YtXudTU1OVmpraGaUBXg2L4he0L+gXALPwkQoAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAyCDYAAMAygs0uAIHtaHm1TlTWauiAMA2LCjO7HABAN0ewQZucrq3X4vyDKnKUN48lxkRrbWac+vcJMbEyAEB3xqUotMni/IMqdro8xoqdLt2df8CkigAAINigDY6WV6vIUS63YXiMuw1DRY5yHXPVmFQZAKC7I9jAZycqa1ucP15BsAEAmINgA59dGtmnxfmhA7iJGABgDoINfDY8uq8SY6JlCwryGLcFBSkxJppXRwEATEOwQZuszYxTgj3KYyzBHqW1mXEmVQQAAC/3Rhv17xOizbdP1DFXjY5X1PA+NgAAv0CwQbsMiyLQAAD8B5eiAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZRBsAACAZXS7j1QwDEOS5Ha7Ta2jaf9m1+GP6I139MY7euMdvfGO3njnb71pqqPp97g3QcbFtrCY+vp6lZWVmV0GAABog9jYWPXs2dPrfLcLNo2Njfrqq6/Uo0cPBQUFmV0OAABoBcMw1NjYqODgYPXo4f1Omm4XbAAAgHVx8zAAALAMgg0AALAMgg0AALAMgg0AALAMgg0AALAMgg0AALAMgg0AALAMgk0nOHLkiObNm6eJEycqISFB9957ryorKyVJy5cv1+jRoxUXF9f85/nnn29+7Pbt25WcnKyxY8cqLS1NBw4cMOswOsU777yjjIwMXXnllUpISFBWVpbq6uokSYcOHVJGRobi4uKUlJSkgoICj8d2595093XTxO12a86cObr//vubx7r7upEu3BfWjLRz505ddtllHj1YsmSJJNZNS70J+LVjoEOdPXvWSEhIMJ588knj3LlzRmVlpXHHHXcYd955p2EYhnHzzTcbL7zwwgUfW1JSYsTFxRn79u0z6uvrjWeeeca46qqrjNra2q48hE5TUVFhxMbGGtu2bTPcbrdx8uRJ46abbjKefPJJ4/Tp08bEiRON5557zmhoaDDefvttIy4uzjh06JBhGN27N4bRvdfNNz3xxBPGqFGjjPvuu88wDKPbr5sm3+6LYbBmDMMwVq1aZdx///3njbNuvPfGMAJ/7XDGpoN99tlnGjVqlBYuXKiePXsqIiJCs2bNUmlpqerr6/W3v/1No0ePvuBjCwoKNG3aNI0bN04hISGaO3euIiIitHPnzi4+is4RGRmpt99+W2lpaQoKCtLp06d17tw5RUZGavfu3QoPD9fs2bMVHBys+Ph4paSkKC8vT1L37k13XzdN3nnnHe3evVs33HBD81h3XzfShfvCmvlaWVnZBXvAuvHeGyusHYJNBxs+fLg2btwom83WPLZr1y5dfvnlOnLkiL766iutWbNGV199taZOnaqnnnpKjY2NkiSn06kRI0Z4fD273a4jR4506TF0pr59+0qSrr32WqWkpCg6OlppaWlyOBwtHnt37g3rRqqoqNCDDz6o1atXKzQ0tHm8u68bb31hzXz9uYCHDx/WG2+8ocmTJysxMVEPPfSQqqqquv26aak3Vlg7BJtOZBiGHn/8ce3Zs0cPPvigzpw5o4kTJ2rOnDn685//rMcee0zPPvusNm3aJEmqqanx+OEkSb1791Ztba0Z5Xeq3bt3q6ioSD169NDixYsveuzduTfdfd00NjZqyZIlmjdvnkaNGuUx153XTUt96e5rRpIqKyt12WWXaerUqdq5c6e2bNmi48ePa8mSJd163Ugt98YKa4dg00mqq6u1ePFiFRYW6rnnntPIkSOVkJCgzZs3a+LEiQoJCdGYMWN06623Np/CCw0Nbb5ZtEldXZ3CwsLMOIRO1bt3bw0cOFBLlizRm2++edFj7869GT16dLdeNxs2bFDPnj01Z86c8+a687ppqS/8rJGioqKUl5en9PR0hYaGavDgwVqyZImKiopkGEa3XTdSy7254oorAn7tEGw6wccff6yZM2equrpaW7du1ciRIyVJr732mrZs2eKxbX19vXr37i1JiomJkcPh8Jh3Op2KiYnpmsI72f79+/XP//zPqq+vbx6rr69XSEiI7HZ7i8fenXtTXFzcrdfNjh079O6772r8+PEaP368XnrpJb300ksaP368RowY0W3XTUt96e4/a6SvL8f9x3/8hwzDaB6rr69Xjx49NGbMmG67bqSWe1NUVBT4a8fce5et5/Tp08Z1111n3H///Ybb7faY2717tzFmzBjj7bffNhobG439+/cbV111lfHiiy8ahmE035n/zjvvNN9tPmHCBOPUqVMmHEnHq66uNq699lrjkUceMc6dO2d88sknRnp6urF8+XKjsrLSGD9+vPHMM88Y9fX1xjvvvNPcC8Po3r3p7uvm2+67777mV/9093XzTd/sC2vGMD7//HNj7NixxlNPPWU0NDQYn376qXHLLbcYS5cu7fbrpqXeWGHtEGw62KZNm4wRI0YYV1xxhTF27FiPP4ZhGPn5+cYNN9xgXHHFFcaUKVOM5557zuPxL774ojF16lRj7NixRnp6unHw4EEzDqPTOBwOY968ecb48eONyZMnG7/97W+Nc+fOGYZhGO+//74xa9YsIy4uzpgyZYqxbds2j8d2595093XzTd/8BW4YrJsm3+4La8Yw9u7d27w2Jk2aZGRlZRl1dXWGYbBuWupNoK+dIMP4xrkoAACAAMY9NgAAwDIINgAAwDIINgAAwDIINgAAwDIINgAAwDIINgAAwDIINgAAwDIINgDwf44fP252CQDaKdjsAgB0L0lJSSovL1dw8Pk/fnJycjR+/HgTqpLy8vL06quv6tlnnzVl/wA6BsEGQJd7+OGHlZaWZnYZHiorK80uAUAH4FIUAL+SlJSkDRs2aMaMGYqLi9OMGTNUUlIiSfrkk080cuRIFRQUKCkpSePGjdO8efP097//XZL0wgsvKCkpyePrzZkzR2vXrpUkORwOzZ49WxMmTNDkyZN13333qbq6Wtu3b9eGDRu0b9++5jNG1dXV+tWvfqVrr71W8fHx+tnPfiaXy9WFnQDQFgQbAH5n27ZtevLJJ/X2229r1KhRWrFihcf8G2+8oRdffFG7du2Sy+XSunXrWvV1H374YcXHx+vdd9/Vtm3b9Je//EUFBQW6+eabdeedd2r8+PHat2+fJGnp0qU6ceKEXnjhBb322mvq27evFi1aJD5eD/BvXIoC0OUefvhhPfLIIx5jgwYNUmFhoSQpPT1dl156qSQpJSVFL774ose2d9xxh/r16yfp6zM8Bw4caNV+e/XqpTfffFM/+MEPFB8frx07dqhHj/P/f1dRUaFdu3bplVde0YABAyR9HXTGjx+vw4cPa/To0T4dL4CuQ7AB0OWWL1/e4j02UVFRzX8PDg4+7yzJxea9eeKJJ7R27Vo9/vjj+vnPf64rr7xSK1asUExMjMd2n376qSTplltu8Ri32Wz65JNPCDaAHyPYALCMHj16qL6+3mPs1KlTkqTGxkb95S9/0d13362lS5fq888/16OPPqr7779f27Zt83jMwIEDJUmvvPKKoqOjm8edTqe+//3vd/JRAGgP7rEBYBk/+MEP5HK5VFJSIsMwtGPHDn300UeSvg49K1eu1BNPPKFz584pMjJSvXr1UkREhKSvL1NVV1fLMAwNHDhQ1113nbKzs3Xq1Ck1NDRo/fr1Sk9P15dffmnmIQK4CIINgC63fPlyxcXFnfcnJyenXV83NjZWCxYs0P3336+JEyeqpKREU6dObZ5/4okn9NFHH+maa67R1VdfrTNnzigrK0uSNHnyZJ0+fVrjxo3Tl19+qd/85jfq16+fZsyYoUmTJunPf/6zNm7c6HEGB4D/CTK4xR8AAFgEZ2wAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBlEGwAAIBl/P8DXZIbNAbOPQAAAABJRU5ErkJggg==",
      "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": 12,
   "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>562690.236</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Optimized:</th>                <td>True</td>         <th>  AIC                </th>      <td>254.540</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Trend:</th>                    <td>None</td>         <th>  BIC                </th>      <td>256.978</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal:</th>                 <td>None</td>         <th>  AICC               </th>      <td>256.540</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Seasonal Periods:</th>         <td>None</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>14:17:13</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>           0.3227497</td> <td>               alpha</td> <td>                True</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>initial_level</th>   <td>           388.10589</td> <td>                 l.0</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                } &      562690.236       \\\\\n",
       "\\textbf{Optimized:}        &         True         & \\textbf{  AIC                } &       254.540         \\\\\n",
       "\\textbf{Trend:}            &         None         & \\textbf{  BIC                } &       256.978         \\\\\n",
       "\\textbf{Seasonal:}         &         None         & \\textbf{  AICC               } &       256.540         \\\\\n",
       "\\textbf{Seasonal Periods:} &         None         & \\textbf{  Date:              } &   Tue, 02 Apr 2024    \\\\\n",
       "\\textbf{Box-Cox:}          &        False         & \\textbf{  Time:              } &       14:17:13        \\\\\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} &            0.3227497 &                alpha &                 True  \\\\\n",
       "\\textbf{initial\\_level}   &            388.10589 &                  l.0 &                 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                         562690.236\n",
       "Optimized:                         True   AIC                            254.540\n",
       "Trend:                             None   BIC                            256.978\n",
       "Seasonal:                          None   AICC                           256.540\n",
       "Seasonal Periods:                  None   Date:                 Tue, 02 Apr 2024\n",
       "Box-Cox:                          False   Time:                         14:17:13\n",
       "Box-Cox Coeff.:                    None                                         \n",
       "==============================================================================\n",
       "                       coeff                 code              optimized      \n",
       "------------------------------------------------------------------------------\n",
       "smoothing_level            0.3227497                alpha                 True\n",
       "initial_level              388.10589                  l.0                 True\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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>468.808566</td>\n",
       "      <td>281.191434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>500</td>\n",
       "      <td>559.563003</td>\n",
       "      <td>-59.563003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>400</td>\n",
       "      <td>540.339064</td>\n",
       "      <td>-140.339064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>650</td>\n",
       "      <td>495.044680</td>\n",
       "      <td>154.955320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>850</td>\n",
       "      <td>545.056456</td>\n",
       "      <td>304.943544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Kysyntä     Ennuste  Ennustevirhe\n",
       "Vuosineljännes                                   \n",
       "2018-12-31          750  468.808566    281.191434\n",
       "2019-03-31          500  559.563003    -59.563003\n",
       "2019-06-30          400  540.339064   -140.339064\n",
       "2019-09-30          650  495.044680    154.955320\n",
       "2019-12-31          850  545.056456    304.943544"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tarkistan viimeisen aikaleiman\n",
    "df.tail()"
   ]
  },
  {
   "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>Ennuste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>643.476878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Ennuste\n",
       "2020-03-31  643.476878\n",
       "2020-06-30  643.476878\n",
       "2020-09-30  643.476878\n",
       "2020-12-31  643.476878\n",
       "2021-03-31  643.476878\n",
       "2021-06-30  643.476878\n",
       "2021-09-30  643.476878\n",
       "2021-12-31  643.476878"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Vuosineljännes'>"
      ]
     },
     "execution_count": 15,
     "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; huomaa, että yksinkertainen eksponentiaalinen tasoitus tuottaa vakioennusteen (suora viiva)\n",
    "df_ennuste['Ennuste'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lisätietoa\n",
    "\n",
    "Data-analytiikka Pythonilla: https://tilastoapu.wordpress.com/python/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}