6 - Hyperlocal Forecasting with the Theta Method & FBProphet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperlocal Forecasting with the Theta Method & FBProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LDj8C3rSEKZu"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip3 install fbprophet scipy==1.2 --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ue2BWp1F_TZk"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.holtwinters import SimpleExpSmoothing\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from fbprophet import Prophet\n",
    "from fbprophet.diagnostics import cross_validation, performance_metrics\n",
    "from fbprophet.plot import plot_cross_validation_metric\n",
    "\n",
    "import _pickle as cPickle\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qJVs1ID3EbQx"
   },
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u_Osx6GIEcsJ"
   },
   "source": [
    "### Load the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 43395,
     "status": "ok",
     "timestamp": 1559999219280,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "Ny33j1SvECBq",
    "outputId": "06129c87-e09c-4524-dcc1-50f51dda838e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 43678,
     "status": "ok",
     "timestamp": 1559999219598,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "r8Y1QYnDEgBQ",
    "outputId": "b1d816d6-1c1a-4275-8324-baed5b4afe63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/My Drive/Colab Notebooks/aiforsea\n"
     ]
    }
   ],
   "source": [
    "cd '/content/gdrive/My Drive/Colab Notebooks/aiforsea'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 58945,
     "status": "ok",
     "timestamp": 1559999234896,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "bKevf8gTEhtd",
    "outputId": "8627acdf-576e-4ab0-f39b-188bac597bf9"
   },
   "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>geohash6</th>\n",
       "      <th>day</th>\n",
       "      <th>demand</th>\n",
       "      <th>in_raw</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minutes</th>\n",
       "      <th>ds</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qp02yc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.653687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qp02z1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.664673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qp02z3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.675659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qp02z9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.686646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qp02zc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.697632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geohash6  day  demand  ...                   ds  latitude  longitude\n",
       "0   qp02yc    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.653687\n",
       "1   qp02z1    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.664673\n",
       "2   qp02z3    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.675659\n",
       "3   qp02z9    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.686646\n",
       "4   qp02zc    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.697632\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('full_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nq2e6_1k76Km"
   },
   "source": [
    "### Add info for FBProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1022,
     "status": "ok",
     "timestamp": 1559999238181,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "CBDcnSnT7-rr",
    "outputId": "d4ce38d9-f02f-43ce-f513-1f5d935a465c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 198 ms, sys: 3.33 ms, total: 201 ms\n",
      "Wall time: 202 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['is_weekend'] = (4 <= (df['day'].values % 7 )) & ((df['day'].values % 7) <= 5)\n",
    "df['is_weekday'] = ~(df['is_weekend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T5MRa6ny8KQd"
   },
   "outputs": [],
   "source": [
    "df['cap']   = 1.0\n",
    "df['floor'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 586,
     "status": "ok",
     "timestamp": 1559999238717,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "VTQAI3-w8FvH",
    "outputId": "039aba61-db37-4dcf-d2cd-3e7702e0abe0"
   },
   "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>geohash6</th>\n",
       "      <th>day</th>\n",
       "      <th>demand</th>\n",
       "      <th>in_raw</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minutes</th>\n",
       "      <th>ds</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>is_weekday</th>\n",
       "      <th>cap</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qp02yc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.653687</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qp02z1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.664673</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qp02z3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.675659</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qp02z9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.686646</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qp02zc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.697632</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geohash6  day  demand  in_raw  ...  is_weekend  is_weekday  cap floor\n",
       "0   qp02yc    1     0.0   False  ...       False        True  1.0   0.0\n",
       "1   qp02z1    1     0.0   False  ...       False        True  1.0   0.0\n",
       "2   qp02z3    1     0.0   False  ...       False        True  1.0   0.0\n",
       "3   qp02z9    1     0.0   False  ...       False        True  1.0   0.0\n",
       "4   qp02zc    1     0.0   False  ...       False        True  1.0   0.0\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5dGw1T3IEmkq"
   },
   "source": [
    "### Get data for a certain location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bOkZb2JhEk9N"
   },
   "outputs": [],
   "source": [
    "location = 'qp09d2' # 'qp09t7' # 'qp09ut' # 'qp02zg' # 'qp096w'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3720,
     "status": "ok",
     "timestamp": 1560000958453,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "pypoR7_KExTc",
    "outputId": "5129c060-3720-4b01-8675-3629ff54fc32"
   },
   "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>day</th>\n",
       "      <th>y</th>\n",
       "      <th>ds</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>is_weekday</th>\n",
       "      <th>cap</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900-02-13 04:30:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900-02-13 04:45:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900-02-13 05:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900-02-13 05:15:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1900-02-13 05:30:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      day    y                   ds  is_weekend  is_weekday  cap  floor\n",
       "1266   44  1.0  1900-02-13 04:30:00       False        True  1.0    0.0\n",
       "1267   44  1.0  1900-02-13 04:45:00       False        True  1.0    0.0\n",
       "1268   44  1.0  1900-02-13 05:00:00       False        True  1.0    0.0\n",
       "1269   44  1.0  1900-02-13 05:15:00       False        True  1.0    0.0\n",
       "1270   44  1.0  1900-02-13 05:30:00       False        True  1.0    0.0"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc = df[(pd.to_datetime('1900-01-31 00:00:00') <= pd.to_datetime(df['ds'])) &\n",
    "            (pd.to_datetime(df['ds']) <= pd.to_datetime('1900-02-13 05:30:00')) &\n",
    "            (df['geohash6'] == location)].reset_index().drop('index', axis=1)\n",
    "df_loc = df_loc.rename(columns={'demand' : 'y'}).drop(['geohash6', 'in_raw',\n",
    "                                                     'weekday', 'hour', 'minutes',\n",
    "                                                     'latitude', 'longitude'], axis=1)\n",
    "df_loc.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xph0_oCkFM26"
   },
   "source": [
    "### Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z3TVWBisE8ZO"
   },
   "outputs": [],
   "source": [
    "df_train = df_loc.iloc[:-5]\n",
    "df_test  = df_loc.iloc[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3amOtFuuFUm_"
   },
   "source": [
    "## The Theta Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5lwvp6qQzEDv"
   },
   "source": [
    "### Decompose into trend and seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2059,
     "status": "ok",
     "timestamp": 1560000963478,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "I1jAhPe9zDea",
    "outputId": "0c4aff88-b3ac-4868-ef05-a5f1e0bad7f0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAIBCAYAAADtfZhrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = sm.tsa.seasonal_decompose(df_train['y'] + 1e-18,\n",
    "                                          model='multiplicative',\n",
    "                                          freq=4*24,\n",
    "                                          two_sided=False,\n",
    "                                          extrapolate_trend=1)\n",
    "fig = decomposition.plot()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lTZ3s0YCEGpM"
   },
   "source": [
    "### Deseasonalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1207,
     "status": "ok",
     "timestamp": 1560001158601,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "gvTNYEwoBjMk",
    "outputId": "0d71cafd-e088-4bb6-9ed3-bd7d756c8e7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f057c2677f0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEBCAYAAAB/rs7oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkBjzf7IrTy1bgGIlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_train['y'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1314,
     "status": "ok",
     "timestamp": 1560001159118,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "S_AFcWaIBp4B",
    "outputId": "7c7915bc-9cbd-4b46-82a4-8fb9708f5dde"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f057c6a85c0>]"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEBCAYAAACXArmGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXlgFdXZ/793y04WQoCEfScQcAmi\nFlHZhFa21losolYUq9SivG7oj4oo1YZWqihoS1Hf1teWIgWEoCwFVEBZAgIhrIFAyAZJCJA99975\n/XEzk5m5M/fOepfk+fyTmzszZ87MPec851nOcywMwzAgCIIgCADWYFeAIAiCCB1IKBAEQRAcJBQI\ngiAIDhIKBEEQBAcJBYIgCIKDhAJBEATBQUKBIAiC4CChQBAEQXCQUCAIgiA4SCgQBEEQHCQUCIIg\nCA57sCvgD7fbjZqaGjgcDlgslmBXhyAIIixgGAZNTU2IjY2F1ap8/h/yQqGmpganTp0KdjUIgiDC\nkv79+6Ndu3aKzw95oeBwOAB4HiwiIkL19bm5ucjIyDC6WgEj3OsPhP8zUP2DC9VfG42NjTh16hQ3\nhiol5IUCazKKiIhAZGSkpjK0XhcqhHv9gfB/Bqp/cKH6a0et2Z0czQRBEAQHCQWCIAiCQ5H5KCsr\nC5s3b0ZRURE2bNiA/v37e53z4osv4uTJk9z/J0+exLJlyzBmzBi89957+Oyzz9CxY0cAwM0334wF\nCxYY9AgEQRCEUSgSCmPGjMHDDz+MBx98UPacxYsXc59PnDiBRx55BCNHjuS+mzp1Kl566SUdVSUI\ngiDMRpFQGDZsmKpCP//8c0yaNElTtBBBEAQRPAz3KTQ2NmLDhg247777BN9nZ2dj0qRJmDlzJg4d\nOmT0bQmCIAgDsDAMwyg9efTo0fjwww8lfQosmzZtwooVK7B27Vruu8uXLyMxMREOhwO7d+/G888/\nj02bNiEpKcnvPRsaGpCbm6u0igRBhBhOF4NFq4pw7y2JuKVfXLCr0+bIyMhQFRJr+DqFNWvWeGkJ\nKSkp3OcRI0YgNTUVp0+fxvDhwxWXq/bBWHJycpCZman6ulAh3OsPhP8zUP31UXW9AUARdh+vw5MP\n3KX6+mDXXy/Bqr/WCbWh5qPS0lLk5ORg0qRJgu/Lysq4z8ePH0dRURF69epl5K0JgghRbDbP4imX\n2x3kmhBKUKQpLFq0CFu2bEF5eTkeffRRJCYmIjs7G7NmzcKcOXMwZMgQAMDatWsxatQoJCQkCK5f\nsmQJjh07BqvVCofDgcWLFwu0B4IgWi/silqXW7GlmggiioTC/PnzMX/+fK/vV6xYIfj/qaeekrw+\nKytLQ9UIgmgVNLstSSiEB7SimSAIU2FFgctFQiEcIKFAEISpsPGNbvIphAUkFAiCMBU26p2sR+EB\nCQWCIAiCg4QCQRCmwl8e+9V3BcGqBqEQEgoEQZgKgxapsOzzw0GsCaEEEgoEQZgL+RLCChIKBEGY\nCsmE8IKEAkEQpqIi5yYRApBQIAiCIDhIKBAEYSqkKIQXJBQIgjAVEgrhBQkFgiBMg2EYvPLBrmBX\ng1ABCQWCIEzD6XKjtKI22NUgVEBCgSAIguAgoUAQhGmQPyH8IKFAEIRpuEkqhB0kFAiCMA+SCWEH\nCQWCIEyDNIXwg4QCQRCmQTIh/CChQBCEaZBMCD8UCYWsrCyMHj0aAwYMwKlTpyTPee+993D77bdj\nypQpmDJlChYuXMgdq6urw7PPPotx48ZhwoQJ2LFjhzG1JwgipKFkeOGHXclJY8aMwcMPP4wHH3zQ\n53lTp07FSy+95PX9ypUrERcXh61bt6KgoAAPPvggtmzZgtjYWG21JggiLCCZEH4o0hSGDRuG1NRU\nzTf58ssvMW3aNABAz549kZGRgW+++UZzeQRBhAekKYQfhvoUsrOzMWnSJMycOROHDh3ivi8uLkaX\nLl24/1NTU1FaWmrkrQmCCEEo+ij8UGQ+UsIDDzyAJ598Eg6HA7t378bs2bOxadMmJCUlGVJ+bm6u\n5mtzcnIMqUOwCPf6A+H/DFR/bVyvc3l9p6Uu9P4Dh2FCISUlhfs8YsQIpKam4vTp0xg+fDjS0tJQ\nVFSE9u3bAwBKSkpw6623qio/IyMDkZGRquuVk5ODzMxM1deFCuFefyD8n4Hqr52Kq3XA2hLBd2rr\nQu9fGw0NDZom04aZj8rKyrjPx48fR1FREXr16gUAmDBhAlatWgUAKCgowNGjRzFy5Eijbk0QRIhC\n1qPwQ5GmsGjRImzZsgXl5eV49NFHkZiYiOzsbMyaNQtz5szBkCFDsGTJEhw7dgxWqxUOhwOLFy/m\ntIfHHnsM8+bNw7hx42C1WvH6668jLi7O1AcjCCL4kFAIPxQJhfnz52P+/Ple369YsYL7nJWVJXt9\nTEwMli5dqqF6BEGEMxR9FH7QimaCIEyDoo/CDxIKBEEQBAcJBYIgAAC7Dhehock7hFQPpCmEH4aF\npBLauXK9HlXXG9ArLSHYVSHaKMfOViDr7wcwLL0Thg3siHvv6G1IuSQTwg/SFEKAp/+4A3Pe3hns\nahBtmJr6JgDAgeNl+HDtUVTXNhpSrtPlNqQcInCQUAgBrtUY0wEJQitWi0X4hfh/jfz5nwcNKYcI\nHCQUCILwwmqMTED+xavGFEQEDBIKIQTFdBPBQqwYUFNsu5BQCCGcLuqJRHCwiKQCTVDaLiQUQogm\np7HhgAShFLG1yE0yoc1CQiGEaHJSpAYRHMSOZtIU2i4kFEIIEgpE0AigT+G7oyX+TyKCBgmFEICN\n9Ggk8xERJLwdzeZJhTc/2Wda2YR+SCiEAKyTz02GXCJIiB3NlJ6i7UJCIYSgfkgEC4OWJRCtABIK\nBEF4awrk3mqzkFAIIcyy47rcDF5f+T2Ona0wpXwi/AmkT4EIbUgohBBmdcOq6/XYn1eGxf/Yb9Id\niHDHgsD5FAxKq0SYBAmFEIDx+mAsrGmAJn9EKCA2VRGhBQmFNgDbBUkmEHIwotZhqqZgWsmEEZBQ\nCCHM6oZcByepQMgglgFGyYTYaIfXd6QphDaKdl7LysrC5s2bUVRUhA0bNqB///5e5yxbtgybNm2C\n1WqFw+HA3LlzMXLkSADAvHnzsGfPHiQlJQEAJkyYgKeeesrAx2gdmOXcYyNJKPackEPc9oxaMxMb\nZUdNXZPgO6PSchPmoEgojBkzBg8//DAefPBB2XOGDh2KmTNnIjo6GidOnMCMGTOwa9cuREVFAQCe\neOIJzJgxw5haE6pghQHJBELMyi9yse7rfLw5e4Qp5UsJFwtJhZBGkVAYNmyY33NYrQAABgwYAIZh\nUFVVhc6dO2uvXRvDrEG7pWOSVCCErPs6H4CEpmBQY5Qqh2RCaGOKT2HdunXo3r27QCB8/PHHmDRp\nEmbPno38/Hwzbhv2mLdOwd1cvinFE60As3wKUlYo8imENoo0BTXs27cP7777Lj766CPuu7lz5yIl\nJQVWqxXr1q3D448/jm3btsFmsykuNzc3V3OdcnJyNF8bCFhhkHf8OKrKIryO663/pasem67T6Qza\nuwj138Afrb3+p06dEvx/7NgxVBR7t0W1NDY2eX3ndrlUv8/W/v5DCUOFwqFDh/DCCy9g+fLl6N27\nN/d9p06duM9Tp07FW2+9hdLSUnTp0kVx2RkZGYiMjFRdp5ycHGRmZqq+LpBY/lUEhmGQPjAdfbsl\nCo4ZUf+CkmtAdhmsNltQ3kU4/Aa+aNX1/+wiAKBfv37A9nLu6/T0QejdJUH3vW3rygAIc2bY7XZV\n77NVv38TaWho0DSZNsx8dOTIEcydOxdLly7F4MGDBcfKysq4z99++y2sVqtAUBAexLHiRuFykfmI\n8I3YzGOET+Fc8VVcr/XWFMh8FNoo0hQWLVqELVu2oLy8HI8++igSExORnZ2NWbNmYc6cORgyZAgW\nLlyI+vp6vPrqq9x1ixcvxoABA/DSSy+hoqICFosFcXFx+OCDD2C3G265CntMczQz5GgmfCP2Zxnh\n35rz9k7J723kaQ5pFI3M8+fPx/z5872+X7FiBfd5zZo1std/8skn6mtGGAYbfUSaAiGHyy0WCubd\nixSF0IZWNIcQRkcfFZdXY9Jz63HywhVP+YaWTrQmXC7jNQU5yHwU2pBQCCGM7oYHjnt8Odv2XQBA\nOWcIecSLzMzUFMh6FNqQUAgFTMpNZGuekTmbHc00QyPkcIl21THVfERSIaQhoRBCGN0Rrc2dz+lk\nBP8ThBixT4GypLZdSCiEEEaHpLKaQVOzpkAygZCDfAoECwmFEMLofrjs88MAWsxHpCkQcgQy+shK\nQiGkIaHQBmhodAIAHDb6uQlpNu46K/jfrIWUANDkdJlWNqEfGiVCiCNnyvHtoSLDy61r8HTCCIfy\nXFNEaFPf6ETV9QbDyisouSb4n3HLnKgQX/sxOKgdhjS0rDiE+GzzCQDAyJuU54RSAwmF1sPz736D\n86XXseHtKaaUr9fRLI5m4pPaIVZX2YS5kKbQSpFyFEY46OduLZwvvW5q+U1OfaoC//qFT9yOR+4d\n1HKQVlGGNDRKtFLEjkMAcNhJUyCU8cZHe3Vd7+RFM8VG2dGjczvufzMjmwj9kFBopUgKBXI0tzqW\nf35Y8rcONmxmXsATgmrjtb3Qqy3Bh0aJVgqb2oKPmRElRHD48rsCnCm8EuxqeNEkEAqA3UZhqOEC\nCYVWyr+3nfL6jrT21okZv+vAHkm6rneKNQUrb6ihdhjSkFBopdD6oLaDGeYjvYLGyXM0WwDYeJoC\naayhDYWktlKkUgmQptA6MSNPkf6Q1JbrrVYLbAxPKFA7DGlIU2ilkKbQdtATzdMhIUq6TM0lehCH\ntEZQ5FvYQEKhlSIlE0htb53oWX2c0C5Sukyd03m+T8FqsaB753aY8eOB6NQ+hjSFEIeEQhuCOmPr\nxKXjh5W7VG+aC0HWVYvHnDlt7AB0SIymyUmIQ0KhlULpidsOvvIMab1W78DdJNIUWCwWmpyEOiQU\nWikDunuHFNJK0taJHqcwwzCSKdV1Rx+5pFUNC22xE/L4FQpZWVkYPXo0BgwYgFOnvGPfAcDlcmHh\nwoUYO3Ysxo0bh9WrVys6RngwY6juLJF0jGRC64TRoykwDOySQkGnT4HnaOYLHY+mQA0xlPEbkjpm\nzBg8/PDDePDBB2XP2bBhAy5cuIAtW7agqqoKU6dOxe23346uXbv6PEZ4MKOP6DEpEOGFnp/a7W5e\nQ+A0rkxA6FMQWzJJJoQ2fjWFYcOGITU11ec5mzZtwv333w+r1Yr27dtj7Nix+Oqrr/weI8xDajZG\nM7TWiVbzUZPThaLL1ZLH9LYVQZoLCDUFIrQxxKdQUlKCtLQ07v/U1FSUlpb6PUaYh9QqVxIJrRO3\nS9sv+/l/TwNo2YSJj5E+Bb4gsMBCk5MQJ2xWNOfm5mq+Nicnx8CamM9X279HSoKD+19L/UtLq7y+\nq66uCdq7CLffQEwo1/90fj5i3L4nWlL1P1Pg3UZY6urrdT3z2XMtGkhubi6K4jxDzbXr19DYxKgu\nO5TfvxLCqf6GCIXU1FQUFxdj6NChAITaga9jasjIyEBkpPRCG1/k5OQgMzNT9XUB5bOLgn+XZZdx\nO2pprX9O4VHgpNA0EBsbE5B30dDkQiRvl7ew+A18EJL157WZ7t17IDOzh+ypcvXfW3AYOCVtPoqM\niNT1zEW1+cB+j9AZOnQIOibFAADWH9iD2nqnqrJD8v2rIFj1b2ho0DSZNsR8NGHCBKxevRputxuV\nlZXYtm0bxo8f7/cYYQ5XrtWjoPia1/eB0Nq3H7iAn8/biGIZWzVhPFoT4vkKRtC7TsHp5DmaBT4F\ncipIsXbnGezPCw2zul9NYdGiRdiyZQvKy8vx6KOPIjExEdnZ2Zg1axbmzJmDIUOGYMqUKTh8+DDu\nueceAMBvfvMbdOvWDQB8HiPMYeaiLYKdr1gCYcndc6QEgGe7yLSUuADckZD6rZXg8nGd7ugjNz8k\nVXiMVjR789GGYwBg2p7bavArFObPn4/58+d7fb9ixQrus81mw8KFCyWv93WMMAfZQSKgDj7q+IHC\nJbNQzB++opaMXKcggFY0hzy0ojmIMAyDs0VXA3e/gN2JOn4gMUNT0B19xE+dzU9zgcC1wzOFVVi6\n6hCt2VEJCYUg8t/9F/DMkp3BroahkMk48PBNNUZdZ6imwA9JDWDyo9f+9h227ruAqzUNAblfa4GE\nggau1TSipq5JdzkFJdcNqI1yAtEXWUeiGRu/zHpzKzbuOmt4ueGOZkezT/OR1tp4kMt9BAROU2Bl\nntFN8Z9bTmLN9tPGFhpCkFDQwIOvfokZC77UXY6vWXWT03tBkW4CqEU3Nhlf/9KKWvxl7VHDywWA\n7F1n8c6/DppSttn4GoB94dt8ZNx+CnwCmSWVfQbxhj96+WzzCXySnWdomaEECQWNaLXjKqWh0fhB\nNZBRHw1NxnZEs/lw7VH8d39hsKuhCZeLwZTn12PtzjPqrvMVkqpbU5AuwAJLwCYnrFAwY4LSmiGh\nEER8xmybYJwPpPPXrXH2SqinockFN9MS1qgUn+YjvesUfGkKAZIK7F2M1hQCwb68Uvz5n8HRXEko\nBBGJjMUc4eqvZdMkOw2O+KB8OfKw/i27TV139pUzSaPvmsOnTyHQmoIZpliY2ybfWLkX2w8ER3Ml\nodCGCMTAyoYf+rJXa4FkgjysUHDYVQoFM9cp+NAUAgX7CE0mmTK1+nLEhFrIbNgkxGuN+DIfae2U\nvlMXmA/7SFrDJOVoMskctWb7aWTvOWdK2YGitt6zGUJdgxMMwyhOJWFmhJBgPwVRmotAaX2s1mqW\nptDkdMNht/k/0Q9mROrpgTSFIOKr72ptJqv/K707HhCY2TZnPjJQUyi+XI2fz9toWHl8PsnOw+Ur\ndaaUzVJcXo3SihrTyq+pbwmP/vpQkeLrzIw+anK5ER8bgZmTBiOxnTCRZaCGQLZ7aQ3Z9YdRbdxX\n2wiGFkFCIYj4mtFpbQwnL1zxcTRwDcxITUFuI5hw4ddv/Rez3txmWvm1PKFQXqVcwPnSvmrrnZj0\n3HrNdXK53OiSEoef3t1X8H0gQ1Jb6mLODXNOlBlSjq8w62BoESQUgogvJV9rW/BpPgpE+2q+h5Ed\nMS46wrCyWiP1vPBlNaHMZkblOF2MpI/DEtBEFx6MmG1X1zXhdx/uEQjdJZ8dxCmfkzBl+PIFkabQ\n1vBpPtLWGHwN/IEQCmwjNlJlj4zQb7dVSqg5/ZTAj8P/19aTiq9jB6O7bzZ+v3Snyw2bVHhdEMLq\nvvq+QHcZO3MK8cPpy17m2Ws1jbrL9hU1RkKhjWHx0UO0DuC+bcHmNzBW3dWauVOKQIajmmV/NhPW\n0dzyv8IULAxw6+DOuH9MP/lTNL57p8sNu8wMONAWkR9OXdYdKSRXZyPaJl9TWLX1pKBMMh+1MXw6\nmrVGH5mYz0YJ7D2++Na8HEUnzleirsHp/0QNGB01FQwqrtYrOq/R6YLdbvXybXVJieU+a52pOp1u\nyRmwBQFcp8Cvj26h4ClN/Dr+tj4X3+eW6Cqb/54+/eoEistbHM9b9p7XVbYWSCgEETN8CsGIbqtv\ndHKDtBkzG3GJLyz9Fll/32/4fQBz1fVAaTxKBaYnpNLqNTnp1z2J+6xVc3K6GGmhYLGg6HI1vvgm\nX1O5WtEbKcReLdaAi8tr8PuP9+kqu0+XBMH/e3NbdmBb+YW6VepGQEJBJfwOp9fU4Ct8UOvg6jt1\ngTk8/Npm/OKVbM89zBj4JIo8XSi/6bwezDQfBcoypTTXT5PTDYfNKtjvABDuf6BdKLhht3lPe9hv\nVqxXv3ewUmrrm3DlmlBbkt30RyFmyvPICOFysY83Bl4Q8CGhoBJ28AOA5979WldZvkItTdEUTGrY\nfEFpjkzwLtQo4WMVOUONDl+s5A1O7gCZppRGFbGaglhl5TuItQoFl0vafCR+32Yw989f4+GFmwXf\n6V997HkPtaaYLUPLj0VCQQf5F7XtmrY3twRz3t7h8xxzVjSb3/jMmGlLvYrrtfr3swDgFSHz903G\npkR+/Pdbuc9mZ9ZlUbr62+lywWG3eWkKfLQGDMiZjwIRScba5PkOeL3ht2wb3H24WFc5UoRabAMJ\nhSDwty9yca74ms9ztE6EfQ0IgXE0e26iNjlbsBAPh1v3XcDhU5cNK58/GAUqskmtpiCeSNh4v51W\nH0uTTPRRpCNw4cV8zIo+MgRKcxE4Kq7W4Yez5qUXYDl2tsLLhukLJblpNIcC+hgQArJ2jV285nYb\nZuIx1UEr8VuUXzUn7YXeMN39eaWKUnI0KfApuN0Mt8CsQXS+UeYjqXUKapP2SXHsbIXPdC5SGBV9\nZAahpikoSoh37tw5zJs3D1VVVUhMTERWVhZ69uwpOOfFF1/EyZMtC2dOnjyJZcuWYcyYMXjvvffw\n2WefoWPHjgCAm2++GQsWLDDuKWTYfaQY676/ggcmNiE22mHafeYt24XEdpH4x2sTFJ2vZBattZ34\nnCUGYEbCOroZxjPw2CScjWoJdJ9RmlBOLXo1hddX7kV8bAT+7/Uf+zxPiabADpIOu9WrPfJ/Mz2O\nZikBILd2QQ3zlu0CANw/pr/ia3Sbj3Rd7a/w0JIKin6hBQsWYPr06di8eTOmT5+OV1991eucxYsX\nY/369Vi/fj2ysrKQkJCAkSNHcsenTp3KHQ+EQACASIdH5okX92hh3ddnfOaCqbqufHNwhwKhoEdt\nlyMwmkLLXQzbU0GmmKWrDukuWmr8N2tWyA7ER8+Ua9YalKygVeJTYAdJh92Kbp3a4ZVf3YLEOE/i\nOru1pX1qWbfBMB4txGb1budK2r5S5PpIt07tvL67eEnffuhtSVPw+wtVVFQgLy8PEydOBABMnDgR\neXl5qKyslL3m888/x6RJkxAREdycNTGRHqFQ16DfKfmfHdJbHW7QsEjLqmD2rN18JG86COTiNcCT\n3dSIziRXxNZ9F3DwxCVdZUv9EmY5hN1uBnnnKvDKB7vxTxXpKNhr5RA7bysVmDJbhILn2tuHpOGB\newYAADokRnPnaYnGYrULu9377doMFApis1dpRQ2+zy1BnIRV4M//1D+B8IXWSVx9g9OvNmbKfu0+\n8PsLlZSUoFOnTrDZPI3HZrOhY8eOKCmRXsXX2NiIDRs24L777hN8n52djUmTJmHmzJk4dMjcH4gl\nOqpZU2hwYveRYlytVj6bV8p/ePvirvv6jKIfUGqW+OPbewr+1zosde3oPUtqKTSw0Udz3t6Jr74r\n0F2mr6ipvcf0rSaV0hR2HizEtn3GryR1uty4cs3TBi+Wqcv86mvgSBKlpj5yutxveXxNgeXeEb2w\n4e0paBfbMpnTsl6G9WtJaQVGBiCIk//95o878PuP98lqU9drtecp8vcaLpSp10Qam1y4/5VsrPzC\n95qN4wXyE3AzMHyTnW3btiEtLQ3p6encdw888ACefPJJOBwO7N69G7Nnz8amTZuQlJTkoyQhubnq\nF7sUXvZ0wO8PHMOaPZXo2iECI9LboU9qJCJU2jabnNLaRn1Di6BZ+cUxXKssweDuMT7Lul5d6/Vd\ngkPYqI4dy0N5kWfGk5OTo7ie165fR+ckB0qveNe3obFJVVlqycnJwfXrwuf4NucM7vtRe133PX9J\nXpifOleCnBzt5kEpAZ2bX4Hc/AokWi9z/gUj3ttTWdsRE+lpd3uPFasqs5FnExdf98Ad8ci74MCW\nQ54Q6ZPnK/Hd3v2CNi6+puK6551dLDyPHLtQiFy40NI+c3OPobxIncZf1+ipa3FxEXJyhFF2xUUt\nwlDN80ude+DgD0iKaxnC2EV7/PU/8TE23DYgDlsOXcUXW/dhYNdor3L8UX6tCaf9BKx8ufMQhveP\nkz0uVf/aBmWmuYKzZ9B09YKic43Ar1BITU1FWVkZXC4XbDYbXC4XLl26hNTUVMnz16xZ46UlpKSk\ncJ9HjBiB1NRUnD59GsOHD1dc0YyMDERGRvo/kUdS0VVg607sy/cMkBfLG7Hq2wqMvaU7nnngJlVl\nRWy8DNR5D042mx1AywykDgnIzBzqsyz75q0AhANZ3z59gG8quP/T09PRKy0BOTk5yMzMVFzPVXu+\nRXw7K0qvCDu61QI4HA5VZSnms4sAgMzMTKza8y1wuWVm0z6pPXdMK1FnK4Bt0mGiCQkJusq2rSkF\nnNJC5YYbb4LDblP9Gwhofjcs7EDgdAHJaf3QMzVeUTE1dU3Avz0x8l16DgTQUu6YO2/FGADdu+fD\narHgr+uOIqZ9T9zQz9PvpOp/vvQasKEU/fv1QeYNXQTHXFGlwK69AIABA9LRt1ui4scFmv1rnxej\nV88eyMzsJTh2qbEAyPGsRlf6Tr3q3/xO+/VPRw/++xO9awAY3KcjnnwgEzuObkK9JRGZmRmqngWA\non0lql2xss8j136u1zYCa3yve/jiT5M1Bz40NDRomkz7nS4nJycjPT0dGzd6dr7auHEj0tPT0b59\ne69zS0tLkZOTg0mTJgm+Lytr2Yzi+PHjKCoqQq9evcSXGw5ray0UqXZanE5y6qPY/ny+xH/ZUmGj\n4h9eq6VHLjulx5YbuOgjFrMXzOmNFPJ1eaNJe/uynC/xvVaFDz+k8tk/S6+kn3JnH4y80TPAny/1\nXXaTLxOPXZ+jmb1GKs2Fw4BoNBali/Qcdht6pMaret8s9Y3+tdC4aIemspVgViScLxTZUF577TV8\n+umnGD9+PD799FMsXLgQADBr1iwcPdqya9DatWsxatQoJCQIEzwtWbIEEydOxOTJkzF//nwsXrxY\noD2YRZTM6kktMctyQ5s4dcHVGv9+C6eLwdC+HQTfiX97rbmPnC5PPps3fn274Hub1WK6S2Fvbom3\nY9mAe/LLFJv9fK3GVQL/6il39hEcM3MTGsCzcYtS+I7MGh/XxcdGwGoBrlX7tp87RY5mPnrXKbDv\nTcp/oNenwPfZKYngYttHYlwkrmrY+0AuqrBbpxZTUVJ8lKKyv/yuAAeOG7Nbm5ko8in06dMHq1ev\n9vp+xYoVgv+feuopyeuzsrI0VE0/cqsntUSXyEUXiMtS4syua3QKIjwAiRmBxsG0vsGFyAgbbuzf\nEXHRDm7gsVktpusJiz7e52Vh05KOAAAgAElEQVRqOHHhCkb0U2d+EMOvt91uFdjXdWsivPf++JQM\nfPFtPic83111CA//JF3mQiGLPtoLN8Pg1cduU3xrNWHMStus1WpBfGwkqvy0QzZflVTaCb0J8VgH\nsFTZDp0rmmvqWmbuSt4J+yiJcZE4W6Q+LY1cv//lPQOx+B8HAHgc/UWXq+F2Mz5zOy3//DAAYMPb\nUwD4twbwU5gHkla9olkuz0pByTXVIWRyoZXi2crV6kZcuS4fEtjkdKOh0YX4WKHzzihNobquiQvJ\n45dplYgZNwO3qKNeqqzFW6uLUaBHveYVKV4luz+vDD+c0heWKrgV714Hjpfh2SU7FV2391gp9uep\nmwX6M/HwUWPGSe0Q69ecUdO8EU9cjHf4Jr/diH9PJbATEanQ0FsHdwYA1X4KlhreBkJK3gk72UpL\niUXltXrVO6XJPT1fcKalxMHtZlCo0iztK1z7Jz/qiQ/njVVVnlG0aqHgS1X1tbcqwzBenUouAZuU\nbCm+LB+pwKr+CXFCp7l4FzYt8f0Mw6Cmnr96u6VMm80SkNVrTrd0yuQX3/tGc5l8bUBqQdTv/vKd\n5rL9GZ/8zR2qaxsVrQuQoqRceQoWNTP2nqnxgo1apKiulR+4LTo1BXbglsoiYLdZ0a9bIhJita1h\n4pvOnE7/dWMnEaxDuqRcXSiw3OTRYgH6NQu2Xmmesn31e6n36Gvip9csqodWLRR8OWl8xSzvP16G\np/+0AzsPeqIZlDqR2AZ4zYdfge0wXp3CAOtRXYMTbjfDbXT/0sPDuGN2qyUwWVJlNmyva9C+AIff\ndyIcodVkH3ptMx4RpWlWipp1M2pWQMfHReB6baPPAZ0dXGOjfGsKWhzNrMCRSy1jtVgkB9szhVV+\nFyPy/TBOBXV7bLIn2ohdra3WryA3ObNaLZxA7ZDgMQX76vdSW6T6slZYApBiXI7Q6mEBZO+xUsH/\nTU43Tpz3hFKytt4DzeaAc8XKbJE3D/TkdvpGtHmO283g2FlPuCnbGcXmI3EbYFT0xWNnK+B2M5y9\nle2MN/RL4Zb8W23WgKxobnK5ESHry9HouOXVOyk+SvIUrdtzKhn0quuEAq2kvAblVZ7EdHoSrV25\n3oBi0cy1vKoOpRWeGWf27nNY8Nfvmuup/MdLjIsEwwC5+cKw5KvVDVwkXk19E2xWi2qfwqXKWryy\nfDcOnxaGCNfWN3E2+xrOfCStDVhlgh7mvvM1Fqzw1vqcLgYnm/smX1NQIigTmxf2sZr5rh/k+6YU\nvsxH/zM9E49NHowb+3uCZr4+6L1p1rGzFXAzjGRwgK/+GDyR0IaFwn/3Fwoa/N/WH8ULS78VdFLW\nUqGkP3ZIiMKLDw3D4N7J2HO0RNAINu46i3nLduHgiUvcLKqd2KcgagZKfQo5J8owb9kuZO8+h+o6\nzyyIbxLgUlkHIPoIAOrqnYiRmH0Cwm0G1cDXcNrLCIWtGlcgK4kw+v6kcOB+4q1tePSNLX6v8zUT\nXPD4bbBaLVi1VZjt89E3tmDWm9sAAB/+5wgOnvTMnNWkmxiW3gnRkXas2X5a8P1v/rgdsxdvB+CZ\nzcfFOCS1aaGmILzvd7klOJpfjj//86BgFv3aiu/xzJKdcLkZbjYfGyUdx2Kx+G7ff1ufK3h32fuv\n4Pml36K8qk5oPvLzTu4b1Zf7nJIYjQE9kvDtD0WCCcR/dp7BvGW7cPSM9CpwuWpaLB6BM/Wuvohw\n2HDr4M44ml8uyGL73dEST7/Pr+H6PR+ffTyIUqHNCgWny43F/2jZ53fTngIAwPWaRk7VY8P1lDil\nbx7YCVERdvxiTH+43Qye/tMOXtnnAHhy0tTwnHBsFILFAlg0/hKsVlJcXi0om4Vtd57Ml/qkghIn\nXV2DU9Zs8AfevsqXrtRi2eeHkX/R/7aa/L7DpnRgtTKWFetyuXDF2vombNunbAWokgiWXXnXudTo\nSuLWWXzN7oeld4LdasH2A4WcE17u/CanW5GphKVzcix6psbj0KnLnFBhGAZXm8NUPVplk6TpCBCa\nXcWO5oqr9dzf7N3nuO/ZVAzXaxpRU9eE6EibbJ4ji8X3BGX9N/mctgQAh856Vlhf4/VNAPhcJPTE\n/GriYO6z1WrBL8b0h9PFCHZMzN7lyV1WVd0AhmG82ric+UgsTB8Y58kbNXNRy2Rh58FCAMCVapdq\nTaFTe99ZEczE8DQX4cSeIyXYtu8CoiJbVOgDxy+hojmfPptCWI3Tt3fzJtzlVXXY/H0BYqMdKGp2\nQJ0uvILo5iR97WI8msKnCyfAZrPinChcTm4Wca74KjZ/fx7dOrVDQlwE5zC/fKUOpy54Blh+RAl/\n0xslwu1s0VWcuViFpHaRiHDY0CstAcWXq3Gu5BqWf34YC2fd7jUg83G63Kioks/5//dNeejTJRFf\nH7qI746WoKD4KmZNHYKEuEjZjlDB29/gJz/qhS17z+M3992Ax3i7mgHAB2uO4OaBHfHRhmO4fKUO\nDrsVqR1iERfjQFoH+RQEfB65dxD+N9t797X3Vv+AUZndBAEKrElDDn+mqWGDOmHPkRK8u+oQfnpX\nH5TwBsITvHw33x0tRlSEuq46fHBnHC+oxJ8+zcG4G+NQWtcygO/PK0VZZS3XBsUINAVRO+Sby/6y\n9ihiox2CSe3eYyUoulyNOJmygWafgp8+9cZHe/Hz0f0E5q39eaUCB7raENM+XT19s7CsGpu/P4/Y\naDvKm4Xc8YJK7DtWip0HL2LV73/CabtyfUbsCO7WuSXn2KY95xAfG4EzzTszXrnuxKlC78AWuXHl\n1cduRebATqqezUjarFCwWj3OrndF6Zf/xcteyarsSkw5t2V4Qu0S20XiyZ8NxYf/OYL3Vx8WnMNq\nIylJ0V62Ti9Hs8w9d+ZcFMzQWPYeK8XeY6WIirChO6+BssXERDlQ1+gCwzCyDniny41n/IRg8gcu\nOa5IxN/fN6ovsnefw+r/Cmd3J85fwXPvfgOLBVj5/+5BSpJ3bpp3V/0AwGNy6ZEaj7WLJwuOf/Lq\nPXhmyU5s3XcBW3kawp/+ryXfzLv/czcnsFmk3vE9t/YQCIXfPXYr3l91APvzyrxCTp9f+q3X9e+v\n/gET7+iNnqnxfoXwCzOG4fml3+BMYRX++KkwN84L77WULT6mhPtG9cXRM+U4ePIS/rOnEkCLkFn0\n8T4AwKSRvSWvFWgKIsHW5HQjMS4SNw/siO0HCrHks4OC42ybH3FDmmzdLBb/oa4XL1XjnX8J++an\nX50A4JlFl1V65w/zR3JCNGZNycCK9bl4f/UPgmP8bMe19U68tuJ7/Hx0P7RPkDZXioPgIh02vDAj\nE3/8NAcfrDkiOJZXWIe8wuMAgHa8CZvcuHLLoM6Kn8kMWr1QmDu1M/68zmPL7pISy83ae6XFI//i\nVSx84nYkJ0Qh0uHZq7bw0nU4nW68+b/70dhsjlDiU+D/kPeO6IVbBnXCtepGzyYmdiuiI+04V3wV\nLheDLh3jvAZm8cxDTg6xSb+WPHsnIuy25sVKEci/eBVOtxspidGCVapsw4uLdsDtZtDodMsu6jvC\ns6u+8qvh+NsXubhUWYtuneJwz609sfKLXFk7sT+G9kvB/WP6o7i8Gg67DTarBUnxUcgvrMKZi1X4\nJDsPJRXVkkKBRSp8EvB09r+9Mg6Fl67DYbfhwPEybmCfOWkwPtpwTHIxl5STWBxOGx8bgd/c2xnJ\nqX1hs1lgs1qQ2C4KFy9dR229Ex+sOYy6BicXsrz5+/M4fPoyVrwyTtYc1LH5Ge02K96ecycKSq7B\navWUHR8biUtXanGtphF7jhQLhJwUcovrLBYLFjx+G86XXkNeXh6GZAxGbLQD12oaUXG1Hm43g8G9\nk6Wv5X0W+zKcTk/I8bMP3IT7RvX17JtgsyA60o7GJhdKK2rhcrkxsKd3GhwWj6PZf6d6Z+5dsNut\nOHXyOIYOGYKLzX0zOSEac9/xmIC2HyjEn/95EJ//YaLf8gBg8p19cFtGKq7VNsJhs8JhtyIywoaC\nkmvYuvcCdh8pxvXaRhwvqETWPw7gD78ZIVmO1MTqzpu6YnDvZFy51gCH3QqbzYJ2MRH4cscB9O7d\nB2u/PiNIg6M13bbZtHqhkBDT8oiPTc7A6ys9ib5+N/NW5OZX4OYBQlNIx2YThtvNYGfORTwwboCm\nNQMdk2LQMUloDpFzkkohd89Gpxvt4yPRr5sww6ycSYcthzUp1dU7ZYUCG+kCALcM6oRV207iUiXQ\nv3sSRgxNw8ovcgWrieWYMWEgUjvEYvjgzrj/5WwAnhTNsdEOr3rf0D8FHZKi8Ul2Hsqr/MT7i/rh\n0ufu5mzcUZF2rmz+uoEhfTzpRKTyTUk5b8VrW2Ii7aixWbwWWw3q5RlQbxnU2SvajEUuMmn8bT25\nzzabFX26CstmtUg3w/gVCj06yyfUs1ot6JWWgMqSCHRvPi85IRq90hJkr2GvYxELNs++zjZYLBau\nTD4+U7ez5fvxKbCw76W8yI5O7WM48yLDMLDbLHC6GPzfZo/2oGY73I7tY7h+zpKcEI3aek+KfTb4\noLHJhY82HJN9BimSE6KRnCCc2PRLi0Lm4M44fPqywEwcYhuucbQpRzO/sScnROOum7v6vSY3vwJM\nACS6uJFJ3ZJhGGzZex6V15THt7PlsM5fJaGbf315LOw2K7fuwmqxcOsDpExXYu7O7IY7b+oqsIP7\nSlXOboakxokLAL3SEjAs3dv2yhd67JoJtqN7dgXzfJZS38XOUbkQWBaPGVJ68OfPBDN5QluptqVk\nlzIz1jj5Wrx2trhK95aa/qKP/F/vcRrzMWKAZdvNc++2LLTMzZcOV9Xy3q1Wi+B96t2e1SzalFDQ\nknHQZg3sdnlsFdkBpbLaycVja8nZxGoKMZEeoeBr4GXXTrAzMnb1sK1ZzQaUOfekN2yXz3nDlu0v\nK6nSX4+/wK1FKHjMbht2ncVPX9yAqusNkr+ruO5yJiv++XIDHBsd88y0GwURWbE+nLB8lGxyb0YW\nTbnFa3uOFKOwrNor67D68v07mv3BCm+2nxixw5+S982ibSyxoL7Rxa2Dev/fP/i5Iji0evMRH5uG\nH1Kp/VMvbCexWa1wutxwMwzKq+qw9ItSXLiahx6d2yEtRVkEDR+27mzGWF9x+e3jozCoV3tOo2JN\nKVaLfHJBKWxSKZN9dDi7aOCWQ2lHjOAJILZs1uz1dfMq9dLKGnRpfp8TR/TCz8f0467JevoOtIuJ\nQE19k997Wi0WLzMUu+Zk4y6PVmW1WgXRwCmJyjZ6USYUFBWlCjmfQr6GhHJS2KwWVYszpWDbJruI\nUNxDtSSTk1t0KYXaHEr8ax567SvERtlRY8De8WbQpoSCrwyGclTXNgVUU/DYSj0zINY5euR0OdZ9\nna+pPLbuSoRCXYMTUZEtTYINo6uubVK1t65UzilfAxyrRUjZ/bUQ4cN8xN6rqcnNzTK7dIwT2IFZ\nf4ESbDb/s16b1cJFsYwb3l3Wwet9ne93ntEnWXFZauALQn57UZNqw3f5+sxHgPf+z+KJ28xJ6jfT\nUbM6vUeqf9+JGP72oaEqEAASCn5ZsT4XY2/pbkJthLCN2jP4uoT2Rh2zQbbcyGb7vq/ZeG29k1tH\nAbQ04hM+kgeWS6xJkDYfyQ9wNqsFVquF2zTli2/zsfKLY1i3eJJggFI6KxaajzxC4NSFK7ipf4pA\nSLADkx4TjNVi8WsbttuseOjH6ejTJUGRH4vDT7Xemn2H8rJUwO8nfKGgxXwphd1m1ZUeBPD2t4hl\njJaftH93ZdsDs4tO1SJe8xGqtCmfgtbMg2pT4mqBbS9sSKTbxcguQJ7x44GKy2VNwpF+NAWny43r\ntY1IivPe8pTdLWv0sG5cOCULu2KWj5RW4U81j7Bb0eR0o+JqHVas86Q5EIfsiVOByJYloSlsP1CI\nX//hv5xpqcnp4t65ntxjkpsXicqzWi2IcNhwd2a3oOykpRZ+Ff2Z9LTgEO2JIcWPb+/p87hYG33p\nfeGaES3vOTrSjvdfGKX6OqUEwgxtBG1KU9CaSsJf88roo1+FZ/P7sCYDF8Nw34nbd3K88s3HWzQF\n30KBzdjJhkPyYTugw+49w5N6N3YpTcGP+cnRLBR+9XpLmgCnm4GNL0sUawo8oSC6r4PnY2DfjRYN\nkkXJtVI+FiX4itgyE77w5W95yT7rZJlFb0px2G1w+hA2bz41wm+fEk8yxKnttcretA7mbWwTJjKh\n7WkKg3rJL6rRyutP/Eh3GazjjdMU3G6uEYnbt9R+BbLlihzNcjO0S5UeM1CyhBOUddbarBY0Kchh\nzx8oWYefP6cpKxT4iH0MiqOPePeyWi0CTcDh4JmPmovXZT5SIhQ0Cp3unePx9P03arpWD0JNwc37\n7NnVj01HrRWp35pPV4nFnWLEq9PFKNUqveumb2c4X4TqYjUxbUpTsFoteGv2HarTwsk10Kyn70BS\nuyhVoWxycJpC88zWV1ZMNU7fFkez56eWm6GVVHgygUrNlFhNwS6lKUi8Gv77emv2Hfhy5wG/dbbb\nbV6mCu97Kevo4vNsNivcoo3qnS6epqDDoqOkTnYdu96Nv62HV0oGs+GbWZ0CoeBGTKRdl2YFSAsF\ndsAcf1sPv2tDAI/giHDYuBX+4YBe53qgaFOaAuvQVDJzW/z0SL/nDOqVjFSD1E0351Nojr/mNSBx\nU1Kz+blS81FpRS0sFunsjOwg4dDgIEyKj8KALv7NXQ6bt535/zafEGTL1Dqh5z8zO6C99+8fuPw5\nejQFqUFJXJpVo/koWMhFHzU2uXTvsQxI+xTY9RBKs4NaLBbERYfXnDZMZELbEgpqHM3JMomwWH52\nd1+fx9XSks3UU0eXm+EEg1jtVGU+av7rEC32EVNWWYv28VGS6jM/06p3WKIxA57DbkWTaPHal3sK\nsPBv32suU8ouzZ/lsskP9cx8xyiITNNqPmJ5ZtpNuq5Xi5z5qNHpNsTP4bDb4HYzgqgtNrLJXxgu\nH1/aZyB2GVQLmY9CEDUTQr5zUGp2bHQQScu+By3mI9aEJJ5hqDEfsSk6uBWgMu3y0pVa2VkaFxll\nt8LNeASW3ECn1eke4bCitsE75zw/LYeaGf2/37xX0qzHrzc7KOnRFJTMbPUKBbn9KcyC/zr4bb+p\nSX5XPTXwV5nbIuwoulyN081hz+L1B77QY5YLBq3KfHTu3DlMmzYN48ePx7Rp01BQUOB1znvvvYfb\nb78dU6ZMwZQpU7Bw4ULuWF1dHZ599lmMGzcOEyZMwI4dOwx7ADWomRHKxWqbBX+HNMDTgNiZhTgO\nPiZSuSxvMUt5ypWaQR08eQm5+RVeZqk3nxrB1cVTRos9Xg6tYb8Ou01ydyp+P1JTcnSkXdLMJkj2\n1vwcZm+Hq0aIS14f4P165bbjvFrdwAUs6IEVCqy/4sk//BdvN6fgVpLvicWXANEz/ur1mcgxc9Jg\n/yeFAIp+gQULFmD69OnYvHkzpk+fjldffVXyvKlTp2L9+vVYv349FixYwH2/cuVKxMXFYevWrfjw\nww8xf/581NT4z8tvNKqEAq9jXCi95nXc6Hhztg3zNQVWKIjVzh6p8pkxvcplhGq5VGdhc8lfFK3H\nYP0QjFgo+BCSXTSk4gA8g0G15O5Uxs6u+OYJIzQFZffUV37A49t51WUnBBVX63DywhWvrMJaiBCl\nHuGjRoCqMTWpwSwh3CstAaMypRcvzv1lYE2EvvD7VisqKpCXl4eJEz35yidOnIi8vDxUVvredYrP\nl19+iWnTpgEAevbsiYyMDHzzzTd+rjIeNbNYvsouZXIxehzp3smzbP6um7oA8Dje2EGr6LJwj+Bo\nFZrCTc2duGUXOe9zuEFf5E9g3xf7/OwiNr6mIH4Pv5o4SHHd+NjtVsktCwUb9hjwztfuPMN9Pl3o\n2alOq3Yjh7g43UJB19Xq4b8PdjMcdv1IP4Wrfn0hTj3CR00Qhdb1H/4wS1MAgJ4yE7rRw8zPmqAU\nv6NLSUkJOnXqBFvzKiKbzYaOHTuipKQE7dsLY/6zs7Oxa9cupKSk4Le//S1uuskj/YqLi9GlSxfu\nvNTUVJSWqtvEPTc3V9X5Uhw7louiWOUD6q9/3BF/+dJ7xS4AlJaWIidHfttJLcyf1gWAJ1XvxYtF\ncNZ433tQt2jk5CjfieueITbc3qcTjh7x7IhVWFiInBzhvshXrzbvEdzUKCi7rtHTaTO6WpGTk4OL\nRR7htHbzPlypduLOjHi886+LgrKOHxPuOsXir85l5Vf8pvXOy8tDeZHx9vVz584iylni8xw177y+\nvkFw/onjebiso95nCqXbmZo6qTm3tqEloqrq2jXBtefPnQFTXai4LCkKCz1RXz8cPoIO8cL3UlZ8\nHjk53u1eqv61NfK7r50+fRqW2ouyx33iJ1ufmncpvuaazI5x/DK1lG8khjmaH3jgATz55JNwOBzY\nvXs3Zs+ejU2bNiEpSf/MAgAyMjIQGem92tYf/Bd8w9Ch6KAwQyUAnC+5BsgIhc6dOyMzU9us2Bdu\nNwOsKkLnzqno1SUB+FqYz31w/66a7lvX4ARWe4RzZmY/wbENB78DSurRLi4GmZmZgmN33N7y+Yrr\nArDvENbs8WiJcx8ZBXwm7Hji6wHPbyD1PZ/XPlvv9xkyBg9Gt07qE5GJ6yimb98+yMxIlT3ut/6i\n8qOiIj3nN38/dOgQdE7WHrrcGFEMfOud19/fO2VR8v75VNc2Ams8QjI2Nk7wLEMyBqGvaFMgtdQ7\nioE9leg/IN2z4Q/v/d39o5u8wrzl6h/7zU7ginTm1r59+yJT47aWEesuoaFJPguqmncJCOvPxJRh\nzR7viDr+O1ZbvhwNDQ2aJtN+dbXU1FSUlZXB5fLMHlwuFy5duoTUVGEnSklJgcPhkfojRoxAamoq\nTp/25JNPS0tDUVHL7lQlJSXo3Dnw+5CqVQt9nW+WHdpqtcBi8di7pRKtaU1KxlaXbz7al1eKA8fL\nOPOEv81TxMeNdMCPvLGL/5NMwmjzkRi9tu+AB60I9mgW3lxNCnU5InyYj4xwZOslsZ1nvwtNExA/\nsO+Tb57+9U+HGH4fPfhtrcnJyUhPT8fGjRsBABs3bkR6erqX6aisrGVT8+PHj6OoqAi9evUCAEyY\nMAGrVq0CABQUFODo0aMYOdL/4jCjUdv5+UKha0ehA9XMYSQ5PgolFTUyW0hqG4jZZ2fTYQPAGyv3\nYuHfvsfBEx5tyJ/tWxwZUlvv7QPQyo+Gys/UWcwau83OUafXHxp4n0LL5+MFlZj0XIsWF2lg9JGU\nUNAbqcWi550ltfOsUZo2tr+fM9VzQ/8U3H1zVzx9/w0APH1u4h36ckkZjSLz0WuvvYZ58+Zh+fLl\niI+PR1ZWFgBg1qxZmDNnDoYMGYIlS5bg2LFjsFqtcDgcWLx4MVJSUgAAjz32GObNm4dx48bBarXi\n9ddfR1yctigVPajVFPhRH14DpokDSVpKHC5fqUOthI1d6+yFHfj2HPFtO/eFeNHc8QLlwQb+yzZv\nl7F+3RI5p7IUxjsWheXp1kR4I9yKV8Zi1pvb9JXnB1/vWU3IqGwZvEy1YtQszDSLmObtUs0QxpEO\nG557MBP5F+XbY7BRJBT69OmD1atXe32/YsUK7jMrKKSIiYnB0qVLNVTPWNR2fr75RjyD0ZpwSwl2\nuxV1DU7UiqJxfnp3X4y/rYemMo0wd4nNR7//eJ/uMrmylQgFjWW//sTt+OXvvpQv12xVQWfx/EVP\nnZNj8ePbeyJJIputUfh6HXEKtxL1RSA0BT0jOpdqRmbvbSNg7yGuZgc/mRQCQZta0ax2Qsi3p4pn\nMC4TGwybY6hGZJ7p3sl/9kg5lFzlL7GfmnBBtSiagWocXONiIpDYLpLbG1eMXkWhd1oCzha3ODzF\n5iKj39vsn99gaHli5NrYtHH9DUn+yE9ffuV6veCYVNr1QMNOfnwlpdSLVDjtX14eg3YGCF29hNc6\ncZ2oVeO780w1Ymfh1n0XDKmTFJ6dqRivuP0GPxvb+0KJMPmfX/qOejBTKPhzcuulyUc2Tb2awlu/\nGSH4n20rbAoMvR3dqKSLeuFvJ6kHvqaw+r+nBcdUafM+fjc9C/7YCYq/HfX0wPUlXj3TOsSRUAg0\nas1HNpsVtwzqBMB7QDQz9QW7XWEtbx/XG/uncAvbtMDvPy8v3+V13GoBUpJ8h+uaae8125bsa09c\nvT4FscmDnQUmxEUYsgK4b9dELHthFP795r26y1KCnPA3qs237Mnt8lqIaZQpT89w3qIpmNfHzVqN\nbQShWzMT0NL52Q4iVvfMTD1gt1u8zEf3j+mny57L72y5+d4x7+LVzJL1MlNTUFK2Sa9cryNYbPKo\naxZADAPDAhK6d45XtZJdDzarBRvenuK1+tY4odCiKSTEBX9mLIbVFJpMFAotuchCj7YlFDR0flaQ\niKOPTBUKNiucTqGmYLRj+6vvCgT/OxTM1M008SgpW88b750mv1OX3smpeLJR1by1qYEyISiItTe1\ne2nIwa5TaGhyYcU6/ZkKpNDTPX05wo2C1S5DMXFqmxIKWjo/m55XPJM1MzU652jm+RQ6JSvbfEQp\nm/eeF/yvZFCWcwY/N/1m3fWJU5AeWo8gfnP2CNw/pp/kMb2agsViwbxHbuH+52zRDGN+ZJOJiE0c\n/rbAVArb1iqu1vs5MzhMGtkb6T3bY9xwbZF+SgiF0Fs52lT0kZYOypqNvMxHZjqh7FZcr22C1WrB\nDb1i8P9mjTLcdCBOv60k+kfOxGOEfbS9gi0Y9cyqYqMdGNhDen9uI8btEUPTuM+sOSkEJ4GqEL+X\nSQYtsmJ9CpevGJs7zCiSE6Kx+LfmLq410xSrl9CtWYjAmo3EG3qYqSmwcelXqxsR5bCaYksWp6l+\n+VfD/V4jp00Ysd2kxWLB28/cial39dFdlhxdO0kvmDQrKybDmL9aOlDERTsMe0/strjlVz1C4cb+\nKYaUKyS0RbJh6zFMIOQ+HtkAAA/bSURBVHRrZiDTxmlfrm4LgqOZn94iMsKcUeVskTCRWL9u/pOc\nyc1ujMod1L97kiKNQSvt20mXbZSJZ8aPBwLgTRgYcxc5mg3/vbSLNdYh7LBbca3ak3SONetpXZgp\nxQ39zBA0xmGzWjCwRxJeenhYsKviRZswH82YkI4ZE9I1XWvnHM3CAdFMocCfkUU5AiO3lQyMcnbQ\nQK030vvOo2Q0LqOE2rSxA1Bd28Q58RkwYa0p8Nuhkm1H1RBht6KkwrPRVlK7KGx4e4phZf/m5zcg\nJiqwW5hq4Y9z7gx2FSRpE5qCHlzNA5E4dM5M8xFfAEUGSCgoQU5TsFgssNsshmw3eGtG4LPnGjlw\nWy0WbiV8KEaWqIEvLJ+bbkw6Zxb+ymgjkuwRxhE6I06IwqZGMCONrhz88NdIR+hMNeWFArB28WT8\n9O6+uu+R1iEO/8maJHnMyDGWr90Y6VOwWi2CCUN4awotnxMNzrXEXxdjRDpus/nfBeODXYWAQULB\nD+ysL5CzGb6zNRCzzeUvjlZ0ntVqwSev3uP1vdFhlw67FX95eQz+9v/GCQ8Y+C74gsDI/RQslhYz\nFxPmIalm1p0f7Wa0UDCj2mb6ukKNNuFT0INbtGk9y7jh5u2pmsRrgIHQFJSsEWBJTvBOhWFGJ0zr\nENd8vyhD49lffGgYSitqUFByDd8c8mz8ZLT5iBMKxhUbFMzcqziCZxaN0CgUbuqfgjM+UqIT2iCh\n4Ad2pi5e0fzghIEBuX+/NPNnKFo7JYuZM0p+yUZs1M7u8NbkdHNCwVhNocV8FO4hqex7eZm3MM8o\n+D4FrcLnwQnpGDe8B554y9z9JdoaJBT8wGoK4oHP7C0cFz89Eg67FVcv5Zt6H0C9ULihXwccPl3O\n/W/qm2h+z0P7dkDXjsb5dRx2a7Opx2CfQnNRHm2BCeuQVLaNmyH02QVsd9yQ5udMeWxWCzoaHBXl\ni7+8PMbUdNqhAvkU/MCuXBb3CzNVawBI79UefRWsHdADuzZB7ZL7RU8KU0WbKSDZoifc1tPwslkt\n0MhBz9LcLhb89Ts0NrnDOvlR5+bUKuxOZEbC+hT0roYPRDj04N7JADwmzUAGnAQL0hT8IDdwhLMD\nkeWNX/8ItfVO/c9i4qtgizZTCBv5U7JlHTp1GYCyRYGhyq8mDsLAHu0xtG8Hw8t2OKQXharF7H74\nxZ8mm1p+KEJCwQ+s+Ug8JoXABlG6iY12IFaFk1kOU01pzWWbKRSMrL+4rHCePDjsNozUsYeH77JZ\nTcGM92Osj6itQeYjP7RmTcEwTHwV7JhhzuDhwejoI0HZxhXdqii6VA0AyBelWyGCDwkFP8RGe5Qp\n8d60JBNaMNWngABoCgaW7TVZoHYiyfnS6wC8c3ARwUeR+ejcuXOYN28eqqqqkJiYiKysLPTs2VNw\nzrJly7Bp0yZYrVY4HA7MnTsXI0d60s/OmzcPe/bsQVJSEgBgwoQJeOqpp4x9EpP47S9uQnrPC0jv\nKUy7bHb0UThh6qtoLtvM922o+Ug0zaJ2EjiG9OmAo/nl/k8kfKJIKCxYsADTp0/HlClTsH79erz6\n6qv4+9//Ljhn6NChmDlzJqKjo3HixAnMmDEDu3btQlSUJ87+iSeewIwZM4x/ApOJj43Az0Z5p28w\nO/oonDAz7JIt2VzzkZFlk6YQLEj+GoNf81FFRQXy8vIwceJEAMDEiRORl5eHyspKwXkjR45EdLRn\nteuAAQPAMAyqqlrXasNYXmge+RRasJhohGSFr7mOZuPKKigRmkOolRDhhl9NoaSkBJ06dYLN5lls\nYrPZ0LFjR5SUlKB9e+mdrNatW4fu3bujc+eWjJcff/wxVq1ahW7duuG5555Dnz7qNlPJzdW+l2tO\nTo7ma/k8O7kTfv9vzyrYQ4cOmjp75WNU/fkM7x9rWLknT5xATbnvhGla79XQ4Elxcfr0KTRePa+p\nDH8cPnLYb4pypfW/dFk4WaqsrDTl91NLKNRBDiV1U3JOdbXHT3HhwnnkOELLjBTK71+M4SGp+/bt\nw7vvvouPPvqI+27u3LlISUmB1WrFunXr8Pjjj2Pbtm2coFFCRkYGIiPVZ2rMyclBZqaBaX+bhcKw\nzMyAmJAMrf9nF7mPv/v1WMPKSk9PR//uSbKn6nmGmO3bgavXkT5wINJ7SU9CNNP8DDffdJPP3e3U\n1H/niRygoJb7Pzk5GZmZ+vew1oPhfcAAHr0Wj4835qFXWrzfuvmtf/PvmDm4O86WnsZtmYMwpI/x\nayu0Eqz339DQoGky7VcopKamoqysDC6XCzabDS6XC5cuXUJqaqrXuYcOHcILL7yA5cuXo3fvlv1c\nO3XqxH2eOnUq3nrrLZSWlqJLF3NioANBOFqPbuqfwi2qMhJzlyk0b3Jk4kbnAVhmQYj42ah+GNCj\nPbp2lN4iVQv3juiFu27uih6d4w0rsy3i1xqcnJyM9PR0bNy4EQCwceNGpKene5mOjhw5grlz52Lp\n0qUYPFi42UpZWRn3+dtvv4XVahUIinAkHH0Kr//6RwCAgT3kZ/VaMPNdcD6FMIk+EhPOuY/MZnDv\nZCTEGbdPg8ViIYFgAIrMR6+99hrmzZuH5cuXIz4+HllZWQCAWbNmYc6cORgyZAgWLlyI+vp6vPrq\nq9x1ixcvxoABA/DSSy+hoqICFosFcXFx+OCDD2C302LqYPDvN++V3SxHK2YOe6xQYExMRG1qlleS\nCQGD3rUxKBqZ+/Tpg9WrV3t9v2LFCu7zmjVrZK//5JNP1NcsRLnzpi5cyuVwxJftXCuBiAwyc7Mh\nI+sf7ltwhjO0JsQYaEWzSv7nlzfj32/eG+xqBB29ezAohe3obhNHWyNlGiOq546cQuMKJ4gAQEJB\nJTab1ZTZdrjx15fHBOQ+rGnH7Q4P85FLJBScbSD/frBhhTotKDUGEgqEJpITotEz1XynHudTMGFs\nNcPaYKbwImQwcTOgtggJBSKkGT7IE6XWIdF7b2i9vPs/d+PRiYMMLfMG0d4DgVrg2JZhXzG9aWMg\nOwihGUsAnMA/vbsvRg/rjsR2xoUusvRKS0CvtARDy/zJiF74cO1R7n8XaQ6m4/E7MRR9ZBCkKRC6\nETtXjcRisZgiEMyCTBhBgMxHhkJCgdAMLcwiQoGWsGXSyoyAhAKhG+qKRDAZ1CsZAEUfGQX5FAjt\nsH2QpAIRROY9cguKL1cjKoKGMyMgTYHQDJlwpWkX48CdJm14T3gTHWlHn66Jwa5Gq4FEK6EbM/MS\nhSOfvfETAAjrdChE24U0BUIznPWIZIIsfbsaG/JKEGZDmgKhmTtu6IIzF6+asrCsNbBy/ji0i4kI\ndjUIQhUkFAjN/GxUX/z4Rz0RE+UIdlVCko5JMcGuAkGohsxHhGYsFgsJBIJoZZBQIAiCIDhIKBAE\nQRAcJBQIgiAIDhIKBEEQBAcJBYIgCIKDhAJBEATBEfLrFNh0uI2NjZrLaGhoMKo6QSHc6w+E/zNQ\n/YML1V897JipNqW4hQnxJOTXr1/HqVOngl0NgiCIsKR///5o166d4vNDXii43W7U1NTA4XDQzkoE\nQRAKYRgGTU1NiI2NhdWq3FMQ8kKBIAiCCBzkaCYIgiA4SCgQBEEQHCQUCIIgCA4SCgRBEAQHCQWC\nIAiCg4QCQRAEwUFCgSAIguBo1ULh3LlzmDZtGsaPH49p06ahoKAg2FUScOXKFcyaNQvjx4/HpEmT\n8PTTT6OyshIA8MMPP2Dy5MkYP348Zs6ciYqKCu46X8eCxfvvv48BAwZwq8/Dpf4NDQ1YsGAB7rnn\nHkyaNAm/+93vAPhuO6HUrnbs2IGpU6diypQpmDx5MrZs2eK3jsGsf1ZWFkaPHi1oK3rqG+hnkaq/\nr34MhE9f4GBaMQ899BCzbt06hmEYZt26dcxDDz0U5BoJuXLlCvP9999z///hD39gXn75ZcblcjFj\nx45l9u/fzzAMwyxbtoyZN28ewzCMz2PBIjc3l3nssceYUaNGMSdPngyr+r/xxhvM73//e8btdjMM\nwzCXL19mGMZ32wmVduV2u5lhw4YxJ0+eZBiGYY4fP87ceOONjMvlCtn679+/nykuLubaipI6hdKz\nSNVfrh8zjO/2Hmp9gaXVCoXy8nImMzOTcTqdDMMwjNPpZDIzM5mKioog10yer776innkkUeYw4cP\nM/feey/3fUVFBXPjjTcyDMP4PBYMGhoamF/84hdMYWEh11HCpf7V1dVMZmYmU11dLfjeV9sJpXbl\ndruZ4cOHMwcOHGAYhmH27dvH3HPPPWFRf/6gqrW+wXwWsVDjw/ZjhvHd3kOpL/AJ+SypWikpKUGn\nTp1gs9kAADabDR07dkRJSQnat28f5Np543a78c9//hOjR49GSUkJ0tLSuGPt27eH2+1GVVWVz2OJ\niYkBr/e7776LyZMno2vXrtx34VL/wsJCJCYm4v3338fevXsRGxuLZ555BlFRUbJth2GYkGlXFosF\n77zzDmbPno2YmBjU1NTgr3/9q8+2H0r1Z9Fa31B8Fn4/Zp8tHPoCn1btUwgn3njjDcTExGDGjBnB\nropiDh06hNzcXEyfPj3YVdGEy+VCYWEhBg0ahP/85z94/vnn8dvf/ha1tbXBrpoinE4n/vKXv2D5\n8uXYsWMHPvjgAzz77LNhU//WSDj2YzGtVlNITU1FWVkZXC4XbDYbXC4XLl26hNTU1GBXzYusrCyc\nP38eH374IaxWK1JTU1FcXMwdr6yshNVqRWJios9jgWb//v3Iz8/HmDFjAAClpaV47LHH8NBDD4VF\n/VNTU2G32zFx4kQAwA033ICkpCRERUXJth2GYUKmXR0/fhyXLl1CZmYmACAzMxPR0dGIjIwMi/qz\n+Oqrvuobas8i7sfss4VDX+DTajWF5ORkpKenY+PGjQCAjRs3Ij09PeRMR0uWLEFubi6WLVuGiIgI\nAEBGRgbq6+tx4MABAMC//vUvTJgwwe+xQPPEE09g165d2L59O7Zv347OnTtj5cqVePzxx8Oi/u3b\nt8ett96K3bt3A/BEslRUVKBnz56ybSeU2lXnzp1RWlqKs2fPAgDy8/NRUVGBHj16hEX9WXzVSeux\nQCPVj4Hw6ct8WnXq7Pz8fMybNw/Xrl1DfHw8srKy0Lt372BXi+P06dOYOHEievbsiaioKABA165d\nsWzZMhw8eBALFixAQ0MDunTpgj/+8Y/o0KEDAPg8FkxGjx6NDz/8EP379w+b+hcWFuKVV15BVVUV\n7HY7nn32Wdx1110+204otasvvvgCK1as4PYamTNnDsaOHRuy9V+0aBG2bNmC8vJyJCUlITExEdnZ\n2ZrrG+hnkar/O++8I9uPAd/tPZT6AkurFgoEQRCEOlqt+YggCIJQDwkFgiAIgoOEAkEQBMFBQoEg\nCILgIKFAEARBcJBQIAiCIDhIKBAEQRAcJBQIgiAIjv8PEGs7z8lknZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['deseasonalized'] = df_train['y'].values / decomposition.seasonal\n",
    "plt.plot(df_train['deseasonalized'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jauiRviN0YqJ"
   },
   "source": [
    "### Prepare train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BcEHPDc4y0OJ"
   },
   "outputs": [],
   "source": [
    "X_train = np.arange(len(df_train)).reshape(-1, 1)\n",
    "X_test  = (np.arange(5) + len(df_train)).reshape(-1, 1)\n",
    "\n",
    "seasonal_test = decomposition.seasonal.values[-4*24:-4*24+5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ApgQZps9v0zB"
   },
   "source": [
    "### Forecast using data from previous cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RqA-rYhuv4Zp"
   },
   "outputs": [],
   "source": [
    "yprev_pred = df_train['y'].values[-4*24: -4*24+5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1756,
     "status": "ok",
     "timestamp": 1560000285503,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "2iVql1fkwOlW",
    "outputId": "55820f8f-67fb-4287-9147-a65ec6a5f4c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 0.0\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(yprev_pred, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1239,
     "status": "ok",
     "timestamp": 1560000286876,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "NpWiazd1wq8F",
    "outputId": "aa6817a0-0210-462d-bc16-8281078e2e8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f057c7bd550>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEBCAYAAABv4kJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(yprev_pred, label='prediction')\n",
    "plt.plot(df_test['y'].values, label='actual')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LdQcDE_RM-VR"
   },
   "source": [
    "### Theta = 0 (w/ linear regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wC1n5FwBDjw7"
   },
   "outputs": [],
   "source": [
    "linreg = HuberRegressor().fit(X_train, df_train['deseasonalized'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 803,
     "status": "ok",
     "timestamp": 1560000290309,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "iTiZvUtvN0v3",
    "outputId": "13849cf3-e767-48b0-b512-bd4ed3522161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta0_pred = linreg.predict(X_test)\n",
    "y0_pred = np.clip(theta0_pred * seasonal_test, a_min=0.0, a_max=1.0)\n",
    "y0_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1060,
     "status": "ok",
     "timestamp": 1560000293902,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "QbXIVLbHvWxW",
    "outputId": "285d667b-3408-4463-fe68-28bf26c4e537"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 0.0\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(y0_pred, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 855,
     "status": "ok",
     "timestamp": 1560000308105,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "rJmvbWQrvaBC",
    "outputId": "383ebb25-df5a-4502-8e32-f473419620b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f057f221b38>"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEBCAYAAABv4kJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y0_pred, label='prediction')\n",
    "plt.plot(df_test['y'].values, label='actual')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lV4wwuy83IlH"
   },
   "source": [
    "### Theta = 2 (w/ exponential smoothing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1195,
     "status": "ok",
     "timestamp": 1560000323583,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "NydOWBcc3MvO",
    "outputId": "6e0dedd7-fcd9-4409-e4de-e82bc6fab9d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f057c60b1d0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEBCAYAAABv4kJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_theta2 = 2*df_train['deseasonalized'].values - linreg.predict(X_train)\n",
    "plt.plot(y_theta2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1051,
     "status": "ok",
     "timestamp": 1560000451677,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "-U6S64223VWa",
    "outputId": "aa0f0e4f-fb9a-4042-a83f-82fe6d8eefc7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.e-09 1.e-09 1.e-09 1.e-09 1.e-09]\n",
      "CPU times: user 26.5 ms, sys: 2.7 ms, total: 29.2 ms\n",
      "Wall time: 31.4 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/holtwinters.py:734: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/holtwinters.py:736: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#ses = ExponentialSmoothing(np.clip(y_theta2, 1e-9, 1.0), trend='add', damped=True).fit()\n",
    "ses = SimpleExpSmoothing(np.clip(y_theta2, 1e-9, 1.0)).fit()\n",
    "theta2_pred = ses.forecast(5)\n",
    "y2_pred = np.clip(theta2_pred * seasonal_test, a_min=0.0, a_max=1.0)\n",
    "print(y2_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1116,
     "status": "ok",
     "timestamp": 1560000459552,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "ad56fRbK3g8d",
    "outputId": "89b699b7-c8f7-49a4-d73f-8f6caae5b283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 9.999999999999996e-10\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(y2_pred, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1076,
     "status": "ok",
     "timestamp": 1560000460026,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "-7KQ7DcM3jU9",
    "outputId": "ce603d95-7cae-4931-9a69-f066c5f3bbc7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f057c5428d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfiIikWHxExGJzP8Bfg8iHC43cz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y0_pred, label='theta = 0 prediction')\n",
    "plt.plot(y2_pred, label='theta = 2 prediction')\n",
    "plt.plot(yprev_pred, label='previous')\n",
    "plt.plot(df_test['y'].values, label='actual')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0N311fEx3sWD"
   },
   "source": [
    "### Weighted theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ADu_WNvsAO8G"
   },
   "outputs": [],
   "source": [
    "weighted1_theta_pred = np.clip((y0_pred + y2_pred)/2.0, a_min=0.0, a_max=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M-dJVlFI3-4C"
   },
   "outputs": [],
   "source": [
    "weighted2_theta_pred = np.clip((yprev_pred + y0_pred + y2_pred)/3.0, a_min=0.0, a_max=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 711,
     "status": "ok",
     "timestamp": 1560000472549,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "Kr6JtrEX3-4N",
    "outputId": "c6f22666-bc9b-41b2-b62a-a0192feea18f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 4.999999999999998e-10\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(weighted1_theta_pred, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 781,
     "status": "ok",
     "timestamp": 1560000473149,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "LJi3meEgU337",
    "outputId": "d494d296-9744-4d78-a6f4-0309781dddb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 3.333333333333332e-10\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(weighted2_theta_pred, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1447,
     "status": "ok",
     "timestamp": 1560000475366,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "BLtAoiJp3-4e",
    "outputId": "241f6c49-5d78-4fd0-c8c6-24837ec6076e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f057c4b9e10>"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y0_pred, label='theta = 0 prediction')\n",
    "plt.plot(y2_pred, label='theta = 2 prediction')\n",
    "plt.plot(yprev_pred, label='previous')\n",
    "plt.plot(weighted1_theta_pred, label='weighted1 theta')\n",
    "plt.plot(weighted2_theta_pred, label='weighted2 theta')\n",
    "plt.plot(df_test['y'].values, label='actual')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vm2HqZgk7gtQ"
   },
   "source": [
    "## FBProphet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rTpFjDGqKwnZ"
   },
   "source": [
    "### Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2289,
     "status": "ok",
     "timestamp": 1560000480513,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "f00A_K239rrg",
    "outputId": "ce98c40c-2f09-4fda-bb70-bf6231a9b76c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f057c54aeb8>"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = Prophet(seasonality_mode='multiplicative')\n",
    "\n",
    "m1.fit(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1148,
     "status": "ok",
     "timestamp": 1560000503790,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "yOPbvOpT7idL",
    "outputId": "f628641a-3875-46de-d493-6eb9bb6b7000"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f057c487b70>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = Prophet(\n",
    "        growth='logistic',\n",
    "        seasonality_mode='additive',\n",
    "        daily_seasonality=False,\n",
    "        uncertainty_samples=1000\n",
    "    ).add_seasonality(\n",
    "        name='weekday_daily',\n",
    "        period=1,\n",
    "        fourier_order=12,\n",
    "        condition_name='is_weekday'\n",
    "    ).add_seasonality(\n",
    "        name='weekend_daily',\n",
    "        period=1,\n",
    "        fourier_order=12,\n",
    "        condition_name='is_weekend'\n",
    "    )\n",
    "\n",
    "m2.fit(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JbZBSuTEKzni"
   },
   "source": [
    "### Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1225,
     "status": "ok",
     "timestamp": 1560000508276,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "5YMwi46Q7tpk",
    "outputId": "b49776f1-4d8c-484f-8425-3630938be834"
   },
   "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>ds</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>is_weekday</th>\n",
       "      <th>cap</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900-02-13 04:30:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900-02-13 04:45:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900-02-13 05:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900-02-13 05:15:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1900-02-13 05:30:00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds  is_weekend  is_weekday  cap  floor\n",
       "0 1900-02-13 04:30:00       False        True  1.0    0.0\n",
       "1 1900-02-13 04:45:00       False        True  1.0    0.0\n",
       "2 1900-02-13 05:00:00       False        True  1.0    0.0\n",
       "3 1900-02-13 05:15:00       False        True  1.0    0.0\n",
       "4 1900-02-13 05:30:00       False        True  1.0    0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_weekend(day):\n",
    "    if type(day) != int:\n",
    "        day = int(pd.to_datetime(day).day)\n",
    "    day %= 7\n",
    "    return (4 <= day <= 5)\n",
    "\n",
    "future = m1.make_future_dataframe(periods=5, freq='15min', include_history=False)\n",
    "  \n",
    "future['is_weekend'] = future['ds'].apply(is_weekend)\n",
    "future['is_weekday'] = ~future['ds'].apply(is_weekend)\n",
    "\n",
    "future['cap'] = 1.0\n",
    "future['floor'] = 0.0\n",
    "\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2924,
     "status": "ok",
     "timestamp": 1560000510624,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "7WGwzpBZ8sAb",
    "outputId": "3dc7e4b9-b761-4229-a5ae-c5782a74e0e7"
   },
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900-02-13 04:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900-02-13 04:45:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900-02-13 05:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900-02-13 05:15:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1900-02-13 05:30:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds  yhat  yhat_lower  yhat_upper\n",
       "0 1900-02-13 04:30:00   0.0         0.0         0.0\n",
       "1 1900-02-13 04:45:00   0.0         0.0         0.0\n",
       "2 1900-02-13 05:00:00   0.0         0.0         0.0\n",
       "3 1900-02-13 05:15:00   0.0         0.0         0.0\n",
       "4 1900-02-13 05:30:00   0.0         0.0         0.0"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast1 = m1.predict(future)\n",
    "forecast1['yhat'] = np.clip(forecast1['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast1['yhat_lower'] = np.clip(forecast1['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast1['yhat_upper'] = np.clip(forecast1['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast1[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5131,
     "status": "ok",
     "timestamp": 1560000514069,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "5CYFQ76jUWRO",
    "outputId": "babc1eed-e848-40e7-a9a5-7df21431376a"
   },
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900-02-13 04:30:00</td>\n",
       "      <td>5.810813e-14</td>\n",
       "      <td>5.810813e-14</td>\n",
       "      <td>5.810813e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900-02-13 04:45:00</td>\n",
       "      <td>5.953014e-14</td>\n",
       "      <td>5.953014e-14</td>\n",
       "      <td>5.953014e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900-02-13 05:00:00</td>\n",
       "      <td>6.058098e-14</td>\n",
       "      <td>6.058098e-14</td>\n",
       "      <td>6.058098e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900-02-13 05:15:00</td>\n",
       "      <td>6.109949e-14</td>\n",
       "      <td>6.109949e-14</td>\n",
       "      <td>6.109949e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1900-02-13 05:30:00</td>\n",
       "      <td>6.110763e-14</td>\n",
       "      <td>6.110763e-14</td>\n",
       "      <td>6.110763e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds          yhat    yhat_lower    yhat_upper\n",
       "0 1900-02-13 04:30:00  5.810813e-14  5.810813e-14  5.810813e-14\n",
       "1 1900-02-13 04:45:00  5.953014e-14  5.953014e-14  5.953014e-14\n",
       "2 1900-02-13 05:00:00  6.058098e-14  6.058098e-14  6.058098e-14\n",
       "3 1900-02-13 05:15:00  6.109949e-14  6.109949e-14  6.109949e-14\n",
       "4 1900-02-13 05:30:00  6.110763e-14  6.110763e-14  6.110763e-14"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast2 = m2.predict(future)\n",
    "forecast2['yhat'] = np.clip(forecast2['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast2['yhat_lower'] = np.clip(forecast2['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast2['yhat_upper'] = np.clip(forecast2['yhat'].values, a_min=0.0, a_max=1.0)\n",
    "forecast2[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4021,
     "status": "ok",
     "timestamp": 1560000514072,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "GC8t1c_a8_RY",
    "outputId": "228ed3f6-a935-4c8e-a408-6feee174b29a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 0.0\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(forecast1['yhat'].values, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3659,
     "status": "ok",
     "timestamp": 1560000514073,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "E_WpbkjNUdXk",
    "outputId": "ad499f4a-cf29-4a9f-d894-0b11d3b8038f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root_mean_squared_error: 6.009615098948024e-14\n"
     ]
    }
   ],
   "source": [
    "rmse = math.sqrt(mean_squared_error(forecast2['yhat'].values, df_test['y'].values))\n",
    "print(\"root_mean_squared_error:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3375,
     "status": "ok",
     "timestamp": 1560000514074,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "NvYT5SBp9O1P",
    "outputId": "d7183947-c80b-4f4b-863c-2a8e8de9f6d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f057c3b1c50>"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y0_pred, label='theta = 0 prediction')\n",
    "plt.plot(y2_pred, label='theta = 2 prediction')\n",
    "plt.plot(yprev_pred, label='previous')\n",
    "plt.plot(weighted1_theta_pred, label='weighted theta')\n",
    "plt.plot(forecast1['yhat'].values, label='fbprophet_1 prediction')\n",
    "plt.plot(forecast2['yhat'].values, label='fbprophet_2 prediction')\n",
    "plt.plot(df_test['y'].values, label='actual')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eExVNWfZK10U"
   },
   "source": [
    "### Compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2449,
     "status": "ok",
     "timestamp": 1560000514075,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "0wQ3o2auI3M8",
    "outputId": "14585ec2-7667-4110-c459-2c541b2a9c3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev cycle rmse: 0.0\n",
      "theta=0 rmse: 0.0\n",
      "theta=2 rmse: 9.999999999999996e-10\n",
      "weighted1 theta rmse: 4.999999999999998e-10\n",
      "weighted2 theta rmse: 3.333333333333332e-10\n",
      "prophet1_rmse: 0.0\n",
      "prophet2_rmse: 6.009615098948024e-14\n",
      "all1_rmse: 3.333333333333332e-10\n",
      "all2_rmse: 3.3335336175777014e-10\n",
      "all3_rmse: 2.5001502131832766e-10\n"
     ]
    }
   ],
   "source": [
    "prev_rmse = math.sqrt(mean_squared_error(yprev_pred, df_test['y'].values))\n",
    "theta0_rmse = math.sqrt(mean_squared_error(y0_pred, df_test['y'].values))\n",
    "theta2_rmse = math.sqrt(mean_squared_error(y2_pred, df_test['y'].values))\n",
    "weighted1_rmse = math.sqrt(mean_squared_error(weighted1_theta_pred, df_test['y'].values))\n",
    "weighted2_rmse = math.sqrt(mean_squared_error(weighted2_theta_pred, df_test['y'].values))\n",
    "fbprophet1_rmse = math.sqrt(mean_squared_error(forecast1['yhat'].values, df_test['y'].values))\n",
    "fbprophet2_rmse = math.sqrt(mean_squared_error(forecast2['yhat'].values, df_test['y'].values))\n",
    "all1_rmse = math.sqrt(mean_squared_error((forecast1['yhat'].values + 2*weighted1_theta_pred)/3.0, df_test['y'].values))\n",
    "all2_rmse = math.sqrt(mean_squared_error((forecast2['yhat'].values + 2*weighted1_theta_pred)/3.0, df_test['y'].values))\n",
    "all3_rmse = math.sqrt(mean_squared_error((forecast1['yhat'].values + forecast2['yhat'].values + 2*weighted1_theta_pred)/4.0, df_test['y'].values))\n",
    "print(\"prev cycle rmse:\", prev_rmse)\n",
    "print(\"theta=0 rmse:\", theta0_rmse)\n",
    "print(\"theta=2 rmse:\", theta2_rmse)\n",
    "print(\"weighted1 theta rmse:\", weighted1_rmse)\n",
    "print(\"weighted2 theta rmse:\", weighted2_rmse)\n",
    "print(\"prophet1_rmse:\", fbprophet1_rmse)\n",
    "print(\"prophet2_rmse:\", fbprophet2_rmse)\n",
    "print(\"all1_rmse:\", all1_rmse)\n",
    "print(\"all2_rmse:\", all2_rmse)\n",
    "print(\"all3_rmse:\", all3_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I4X78vkqLJi9"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MEdg31YPRr94"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "aniO2f1rN0VU",
    "pM3j0jHBN2k-",
    "zq6rHLW--UOA",
    "HmCrEzZb6Cg2",
    "tzAm7Fsg7PiR"
   ],
   "name": "6 - The Theta Method + FBProphet.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}