notebook_examples/tspDB Example-Multiple Time Series (Real-world Data).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the packages\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "from sqlalchemy import create_engine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bulk_insert(df, table_name, conn_string):\n",
    "    conn = psycopg2.connect(conn_string)\n",
    "    cur = conn.cursor()\n",
    "    output = io.StringIO()\n",
    "    df.to_csv(output, sep='\\t', header=False, index=True)\n",
    "    output.seek(0)\n",
    "    cur.copy_from(output,table_name, null=\"\")\n",
    "    conn.commit()\n",
    "    conn.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tspDB Running Example\n",
    "In this notebook, we showcase the core  functionalities of **tspDB** on a Real world dataset\n",
    "\n",
    "In particular, we show:\n",
    "\n",
    "**(1)**  How a prediction index is created on multiple time series\n",
    "\n",
    "**(2)** How predictions are produced with uncertainity quantification, \n",
    "\n",
    "**(3)**  How does the predictions and confidence interval compare with the observations and their underlining means.\n",
    "\n",
    "\n",
    "### To run this test, you need:\n",
    "\n",
    "**(1)** tspDB installed on a PostgreSQL Database\n",
    "\n",
    "**(2)** psycopg2, pandas, sqlalchemy and matplotlib packages installed\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "The dataset we will be using is a real world dataset available at the [UCI repository](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014#). The dataset describes the 15-minutes load for 370 houses. We will aggregate the data into horly data and split it into a training set with  25968 time-points, and a test set that has 168 datapoints.  The forecasting task here is to perform rolling window hourly forecasting for the next 7 days by forecasting one day at a time. First we will download the data from the UCI repository. \n",
    "\n",
    "**NOTE:** The dataset size is ~ 250 MB, so it might take a while depending on your connection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip\n",
    "!unzip LD2011_2014.txt.zip\n",
    "# change commas to dots\n",
    "!sed 's/,/./g' LD2011_2014.txt > LD2011_2014_clean.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now load and preprocess the data. Specifically we will:\n",
    "\n",
    "1- Pick the first 20 houses `MT-001`to `MT-020`\n",
    "\n",
    "1- Aggregate the data into hourly readings by avergaing the 15-minutes readings.\n",
    "\n",
    "2- Remove the data before 2011, since most of its readings are zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded..\n",
      "data aggregated..\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('LD2011_2014_clean.txt', delimiter = ';')\n",
    "#remove data before 2012\n",
    "data = data.iloc[8760*4:]\n",
    "print('data loaded..')\n",
    "data_2 = data.copy()\n",
    "#pick the first 20 houses\n",
    "data_2 = data_2.iloc[:,:21]\n",
    "# Aggregate\n",
    "data_2['time'] =pd.to_datetime(data_2['Unnamed: 0']).dt.ceil('1h') \n",
    "data_2 = data_2.drop(['Unnamed: 0'], axis = 1)\n",
    "agg_dict = {}\n",
    "for col in data_2.columns[:-1]:\n",
    "    agg_dict[col] ='mean'\n",
    "data_3 = data_2.groupby(['time']).agg(agg_dict)\n",
    "print('data aggregated..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now each column of the dataframe `data_3` contains hourly reading starting from `2012-01-01 01:00` until `2015-01-01 01:00`. In this experiment, as mentioned before, we will split the observations into a training and testing sets. Specifically, the training set will start at  `2012-01-01 01:00` until `2014-12-18 00:00`. and the test set will have the rest of the observations.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Splitting the data into training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_3.iloc[:25968]\n",
    "data_test = data_3.iloc[25968:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will upload the this dataset into your PostgreSQL database. To do so, we will first set up connection to the database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DB connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here, you need to fill in your database connection information below**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fill your db information\n",
    "database = '' # FILL IN your db name where tspdb is installed\n",
    "user = 'postgres' #  FILL IN  user name here\n",
    "password = '00' #  # FILL IN  password here\n",
    "host = 'localhost' #  # FILL IN  host name\n",
    "conn_string = \"host='%s' dbname='%s' user='%s' password='%s'\" %(host, database, user, password)\n",
    "driver=\"postgresql\"\n",
    "engine = create_engine(driver + '://' + user + ':' + password + '@' + host + '/' + database)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Time Series Table\n",
    "Now, we will load the dataset into your database. We will store it in table `electricity_20`\n",
    "\n",
    "**Note:** Given the size of the data, this may take a while"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created and Loaded\n"
     ]
    }
   ],
   "source": [
    "conn = psycopg2.connect(conn_string)\n",
    "\n",
    "cur = conn.cursor()\n",
    "\n",
    "# Drop the table if it already exists\n",
    "cur.execute(\"DROP table IF EXISTS electricity_20;\")\n",
    "conn.commit()\n",
    "conn.close()\n",
    "# load data into Pandas dataframe\n",
    "data_train.to_sql('electricity_20', engine, index=True, index_label='time')\n",
    "data_train.to_csv('ss.csv')\n",
    "print('Table created and Loaded')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the table is loaded into the database. Let's check the last 10 entries of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>MT_001</th>\n",
       "      <th>MT_002</th>\n",
       "      <th>MT_003</th>\n",
       "      <th>MT_004</th>\n",
       "      <th>MT_005</th>\n",
       "      <th>MT_006</th>\n",
       "      <th>MT_007</th>\n",
       "      <th>MT_008</th>\n",
       "      <th>MT_009</th>\n",
       "      <th>...</th>\n",
       "      <th>MT_011</th>\n",
       "      <th>MT_012</th>\n",
       "      <th>MT_013</th>\n",
       "      <th>MT_014</th>\n",
       "      <th>MT_015</th>\n",
       "      <th>MT_016</th>\n",
       "      <th>MT_017</th>\n",
       "      <th>MT_018</th>\n",
       "      <th>MT_019</th>\n",
       "      <th>MT_020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-12-18 00:00:00</td>\n",
       "      <td>2.220812</td>\n",
       "      <td>20.092461</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>159.044715</td>\n",
       "      <td>89.329268</td>\n",
       "      <td>293.898810</td>\n",
       "      <td>6.500848</td>\n",
       "      <td>266.835017</td>\n",
       "      <td>55.944056</td>\n",
       "      <td>...</td>\n",
       "      <td>49.180328</td>\n",
       "      <td>148.404255</td>\n",
       "      <td>49.336650</td>\n",
       "      <td>45.628590</td>\n",
       "      <td>56.689050</td>\n",
       "      <td>42.141230</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>279.552716</td>\n",
       "      <td>10.050251</td>\n",
       "      <td>54.185022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-17 23:00:00</td>\n",
       "      <td>2.538071</td>\n",
       "      <td>21.870555</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>199.186992</td>\n",
       "      <td>99.085366</td>\n",
       "      <td>334.077381</td>\n",
       "      <td>6.783493</td>\n",
       "      <td>316.498316</td>\n",
       "      <td>70.367133</td>\n",
       "      <td>...</td>\n",
       "      <td>55.886736</td>\n",
       "      <td>157.446809</td>\n",
       "      <td>46.227197</td>\n",
       "      <td>52.169751</td>\n",
       "      <td>65.599174</td>\n",
       "      <td>50.398633</td>\n",
       "      <td>61.994609</td>\n",
       "      <td>348.242812</td>\n",
       "      <td>11.055276</td>\n",
       "      <td>64.977974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-12-17 22:00:00</td>\n",
       "      <td>2.538071</td>\n",
       "      <td>23.648649</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>198.678862</td>\n",
       "      <td>94.512195</td>\n",
       "      <td>362.351190</td>\n",
       "      <td>6.500848</td>\n",
       "      <td>362.794613</td>\n",
       "      <td>72.115385</td>\n",
       "      <td>...</td>\n",
       "      <td>58.867362</td>\n",
       "      <td>164.893617</td>\n",
       "      <td>57.628524</td>\n",
       "      <td>56.796426</td>\n",
       "      <td>67.794421</td>\n",
       "      <td>56.520501</td>\n",
       "      <td>73.000898</td>\n",
       "      <td>416.932907</td>\n",
       "      <td>13.316583</td>\n",
       "      <td>71.585903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-12-17 21:00:00</td>\n",
       "      <td>2.220812</td>\n",
       "      <td>25.604552</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>233.231707</td>\n",
       "      <td>97.560976</td>\n",
       "      <td>412.946429</td>\n",
       "      <td>7.207462</td>\n",
       "      <td>420.033670</td>\n",
       "      <td>93.094406</td>\n",
       "      <td>...</td>\n",
       "      <td>70.603577</td>\n",
       "      <td>176.063830</td>\n",
       "      <td>78.772803</td>\n",
       "      <td>54.403318</td>\n",
       "      <td>72.701446</td>\n",
       "      <td>59.225513</td>\n",
       "      <td>70.979335</td>\n",
       "      <td>442.492013</td>\n",
       "      <td>14.321608</td>\n",
       "      <td>66.519824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-12-17 20:00:00</td>\n",
       "      <td>2.220812</td>\n",
       "      <td>26.315789</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>232.723577</td>\n",
       "      <td>105.182927</td>\n",
       "      <td>476.934524</td>\n",
       "      <td>7.348785</td>\n",
       "      <td>412.457912</td>\n",
       "      <td>102.709790</td>\n",
       "      <td>...</td>\n",
       "      <td>75.074516</td>\n",
       "      <td>206.382979</td>\n",
       "      <td>97.222222</td>\n",
       "      <td>65.730696</td>\n",
       "      <td>80.836777</td>\n",
       "      <td>52.676538</td>\n",
       "      <td>78.840970</td>\n",
       "      <td>466.453674</td>\n",
       "      <td>17.336683</td>\n",
       "      <td>67.841410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2014-12-17 19:00:00</td>\n",
       "      <td>2.220812</td>\n",
       "      <td>24.359886</td>\n",
       "      <td>2.172024</td>\n",
       "      <td>209.349593</td>\n",
       "      <td>106.707317</td>\n",
       "      <td>398.065476</td>\n",
       "      <td>6.500848</td>\n",
       "      <td>395.622896</td>\n",
       "      <td>89.597902</td>\n",
       "      <td>...</td>\n",
       "      <td>71.907601</td>\n",
       "      <td>198.404255</td>\n",
       "      <td>112.976783</td>\n",
       "      <td>70.516911</td>\n",
       "      <td>84.065083</td>\n",
       "      <td>45.985194</td>\n",
       "      <td>77.717880</td>\n",
       "      <td>416.134185</td>\n",
       "      <td>14.447236</td>\n",
       "      <td>66.960352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2014-12-17 18:00:00</td>\n",
       "      <td>2.538071</td>\n",
       "      <td>23.293030</td>\n",
       "      <td>2.606429</td>\n",
       "      <td>148.373984</td>\n",
       "      <td>91.158537</td>\n",
       "      <td>258.928571</td>\n",
       "      <td>5.370266</td>\n",
       "      <td>352.693603</td>\n",
       "      <td>66.433566</td>\n",
       "      <td>...</td>\n",
       "      <td>46.758569</td>\n",
       "      <td>207.446809</td>\n",
       "      <td>136.401327</td>\n",
       "      <td>73.388641</td>\n",
       "      <td>79.674587</td>\n",
       "      <td>38.012528</td>\n",
       "      <td>68.733154</td>\n",
       "      <td>394.568690</td>\n",
       "      <td>10.050251</td>\n",
       "      <td>58.149780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2014-12-17 17:00:00</td>\n",
       "      <td>2.855330</td>\n",
       "      <td>22.581792</td>\n",
       "      <td>1.737619</td>\n",
       "      <td>104.166667</td>\n",
       "      <td>67.073171</td>\n",
       "      <td>199.404762</td>\n",
       "      <td>3.674392</td>\n",
       "      <td>293.771044</td>\n",
       "      <td>44.580420</td>\n",
       "      <td>...</td>\n",
       "      <td>32.600596</td>\n",
       "      <td>180.851064</td>\n",
       "      <td>133.913765</td>\n",
       "      <td>63.975750</td>\n",
       "      <td>68.310950</td>\n",
       "      <td>26.195900</td>\n",
       "      <td>52.111411</td>\n",
       "      <td>293.929712</td>\n",
       "      <td>7.035176</td>\n",
       "      <td>45.594714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2014-12-17 16:00:00</td>\n",
       "      <td>4.124365</td>\n",
       "      <td>23.470839</td>\n",
       "      <td>1.520417</td>\n",
       "      <td>101.626016</td>\n",
       "      <td>66.158537</td>\n",
       "      <td>180.059524</td>\n",
       "      <td>3.815715</td>\n",
       "      <td>275.252525</td>\n",
       "      <td>43.269231</td>\n",
       "      <td>...</td>\n",
       "      <td>38.748137</td>\n",
       "      <td>164.893617</td>\n",
       "      <td>142.827529</td>\n",
       "      <td>69.240587</td>\n",
       "      <td>65.470041</td>\n",
       "      <td>24.060364</td>\n",
       "      <td>48.966757</td>\n",
       "      <td>280.351438</td>\n",
       "      <td>8.040201</td>\n",
       "      <td>40.308370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2014-12-17 15:00:00</td>\n",
       "      <td>1.903553</td>\n",
       "      <td>23.826458</td>\n",
       "      <td>1.520417</td>\n",
       "      <td>99.593496</td>\n",
       "      <td>60.060976</td>\n",
       "      <td>197.916667</td>\n",
       "      <td>3.391747</td>\n",
       "      <td>287.878788</td>\n",
       "      <td>44.580420</td>\n",
       "      <td>...</td>\n",
       "      <td>38.002981</td>\n",
       "      <td>154.787234</td>\n",
       "      <td>156.509121</td>\n",
       "      <td>70.835992</td>\n",
       "      <td>69.860537</td>\n",
       "      <td>23.917995</td>\n",
       "      <td>51.886792</td>\n",
       "      <td>301.118211</td>\n",
       "      <td>5.904523</td>\n",
       "      <td>41.409692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time    MT_001     MT_002    MT_003      MT_004      MT_005  \\\n",
       "0 2014-12-18 00:00:00  2.220812  20.092461  1.737619  159.044715   89.329268   \n",
       "1 2014-12-17 23:00:00  2.538071  21.870555  1.737619  199.186992   99.085366   \n",
       "2 2014-12-17 22:00:00  2.538071  23.648649  1.737619  198.678862   94.512195   \n",
       "3 2014-12-17 21:00:00  2.220812  25.604552  1.737619  233.231707   97.560976   \n",
       "4 2014-12-17 20:00:00  2.220812  26.315789  1.737619  232.723577  105.182927   \n",
       "5 2014-12-17 19:00:00  2.220812  24.359886  2.172024  209.349593  106.707317   \n",
       "6 2014-12-17 18:00:00  2.538071  23.293030  2.606429  148.373984   91.158537   \n",
       "7 2014-12-17 17:00:00  2.855330  22.581792  1.737619  104.166667   67.073171   \n",
       "8 2014-12-17 16:00:00  4.124365  23.470839  1.520417  101.626016   66.158537   \n",
       "9 2014-12-17 15:00:00  1.903553  23.826458  1.520417   99.593496   60.060976   \n",
       "\n",
       "       MT_006    MT_007      MT_008      MT_009  ...     MT_011      MT_012  \\\n",
       "0  293.898810  6.500848  266.835017   55.944056  ...  49.180328  148.404255   \n",
       "1  334.077381  6.783493  316.498316   70.367133  ...  55.886736  157.446809   \n",
       "2  362.351190  6.500848  362.794613   72.115385  ...  58.867362  164.893617   \n",
       "3  412.946429  7.207462  420.033670   93.094406  ...  70.603577  176.063830   \n",
       "4  476.934524  7.348785  412.457912  102.709790  ...  75.074516  206.382979   \n",
       "5  398.065476  6.500848  395.622896   89.597902  ...  71.907601  198.404255   \n",
       "6  258.928571  5.370266  352.693603   66.433566  ...  46.758569  207.446809   \n",
       "7  199.404762  3.674392  293.771044   44.580420  ...  32.600596  180.851064   \n",
       "8  180.059524  3.815715  275.252525   43.269231  ...  38.748137  164.893617   \n",
       "9  197.916667  3.391747  287.878788   44.580420  ...  38.002981  154.787234   \n",
       "\n",
       "       MT_013     MT_014     MT_015     MT_016     MT_017      MT_018  \\\n",
       "0   49.336650  45.628590  56.689050  42.141230  47.169811  279.552716   \n",
       "1   46.227197  52.169751  65.599174  50.398633  61.994609  348.242812   \n",
       "2   57.628524  56.796426  67.794421  56.520501  73.000898  416.932907   \n",
       "3   78.772803  54.403318  72.701446  59.225513  70.979335  442.492013   \n",
       "4   97.222222  65.730696  80.836777  52.676538  78.840970  466.453674   \n",
       "5  112.976783  70.516911  84.065083  45.985194  77.717880  416.134185   \n",
       "6  136.401327  73.388641  79.674587  38.012528  68.733154  394.568690   \n",
       "7  133.913765  63.975750  68.310950  26.195900  52.111411  293.929712   \n",
       "8  142.827529  69.240587  65.470041  24.060364  48.966757  280.351438   \n",
       "9  156.509121  70.835992  69.860537  23.917995  51.886792  301.118211   \n",
       "\n",
       "      MT_019     MT_020  \n",
       "0  10.050251  54.185022  \n",
       "1  11.055276  64.977974  \n",
       "2  13.316583  71.585903  \n",
       "3  14.321608  66.519824  \n",
       "4  17.336683  67.841410  \n",
       "5  14.447236  66.960352  \n",
       "6  10.050251  58.149780  \n",
       "7   7.035176  45.594714  \n",
       "8   8.040201  40.308370  \n",
       "9   5.904523  41.409692  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets query our table mixturets2 to inspect its column \n",
    "conn = psycopg2.connect(conn_string)\n",
    "df = pd.read_sql_query(\"select * from electricity_20 order by time DESC limit 10;\", conn)\n",
    "conn.close()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, using **tspDB**, we will be able to forecast *future values*! To do that, we must first create a **prediction index**,and that's what we would do next\n",
    "\n",
    "\n",
    "## Create prediction index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will create a predictive index which we will call `pindex_e20` on all data column [`MT_001`, `MT_002`,`MT_003`...`MT_020`] The creatin of this index allows for retreiving predicted values for entries that are: \n",
    "\n",
    "**(1)** At a future time step (i.e. forecasting), \n",
    "\n",
    "**(2)** Missing/corrupted by noise (i.e. imputation)\n",
    "\n",
    "In addition to that, it will also estimate the underlying time-varying variance of the time series, which allow users to produce upper and lower bound for their prediction.  \n",
    "\n",
    "Let's now build out pindex on relation **electricity_20**, using the time column **time** to predict values for all columns [`MT_001`, `MT_002`,`MT_003`...`MT_020`]\n",
    "\n",
    "**Note: Creating the prediction index will take a while, similar to the time taken to write the data into postgreSQL**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formated query :  select create_pindex('electricity_20', 'time','{\"MT_001\",\"MT_002\",\"MT_003\",\"MT_004\",\"MT_005\",\"MT_006\",\"MT_007\",\"MT_008\",\"MT_009\",\"MT_010\",\"MT_011\",\"MT_012\",\"MT_013\",\"MT_014\",\"MT_015\",\"MT_016\",\"MT_017\",\"MT_018\",\"MT_019\",\"MT_020\"}','pindex_e20', k=> 20);\n"
     ]
    }
   ],
   "source": [
    "# get all columns and format it\n",
    "cols = '{\"'+'\",\"'.join(data_train.columns[:])+'\"}'\n",
    "query = \"\"\"select create_pindex('electricity_20', 'time','%s','pindex_e20', k=> 20);\"\"\"%cols\n",
    "print('formated query : ',query )\n",
    "conn = psycopg2.connect(conn_string)\n",
    "cursor = conn.cursor()\n",
    "cursor.execute(query)\n",
    "conn.commit()\n",
    "conn.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that, the prediction indiex *is built*. Now lets list the predictive indices that has been built so far using the function ``list_pindices()``. \n",
    "\n",
    "Note that there might be others pindices built if you used the `tspdb_test(`) function when installing the package.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>index_name</th>\n",
       "      <th>value_columns</th>\n",
       "      <th>relation</th>\n",
       "      <th>time_column</th>\n",
       "      <th>initial_timestamp</th>\n",
       "      <th>last_timestamp</th>\n",
       "      <th>agg_interval</th>\n",
       "      <th>uncertainty_quantification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>mixturets_var_pindex</td>\n",
       "      <td>[ts]</td>\n",
       "      <td>mixturets_var</td>\n",
       "      <td>time</td>\n",
       "      <td>0</td>\n",
       "      <td>99999</td>\n",
       "      <td>1 units</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>basic_pindex</td>\n",
       "      <td>[ts]</td>\n",
       "      <td>ts_basic_ts_5_5</td>\n",
       "      <td>time</td>\n",
       "      <td>2012-10-01 00:00:00</td>\n",
       "      <td>2012-10-06 18:53:15</td>\n",
       "      <td>00:00:05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>pindex_e20</td>\n",
       "      <td>[MT_001, MT_002, MT_003, MT_004, MT_005, MT_00...</td>\n",
       "      <td>electricity_20</td>\n",
       "      <td>time</td>\n",
       "      <td>2012-01-01 01:00:00</td>\n",
       "      <td>2014-12-18 00:00:00</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>pindex</td>\n",
       "      <td>[ts_7]</td>\n",
       "      <td>synth_data</td>\n",
       "      <td>time</td>\n",
       "      <td>0</td>\n",
       "      <td>99999</td>\n",
       "      <td>1 units</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>pindex_electricity_20</td>\n",
       "      <td>[MT_001, MT_002, MT_003, MT_004, MT_005, MT_00...</td>\n",
       "      <td>electricity_20</td>\n",
       "      <td>time</td>\n",
       "      <td>2012-01-01 01:00:00</td>\n",
       "      <td>2013-04-29 09:00:00</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>mixturets2_pindex</td>\n",
       "      <td>[ts]</td>\n",
       "      <td>mixturets2</td>\n",
       "      <td>time</td>\n",
       "      <td>0</td>\n",
       "      <td>100000</td>\n",
       "      <td>1 units</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index_name                                      value_columns  \\\n",
       "0   mixturets_var_pindex                                               [ts]   \n",
       "1           basic_pindex                                               [ts]   \n",
       "2             pindex_e20  [MT_001, MT_002, MT_003, MT_004, MT_005, MT_00...   \n",
       "3                 pindex                                             [ts_7]   \n",
       "4  pindex_electricity_20  [MT_001, MT_002, MT_003, MT_004, MT_005, MT_00...   \n",
       "5      mixturets2_pindex                                               [ts]   \n",
       "\n",
       "          relation time_column    initial_timestamp       last_timestamp  \\\n",
       "0    mixturets_var        time                    0                99999   \n",
       "1  ts_basic_ts_5_5        time  2012-10-01 00:00:00  2012-10-06 18:53:15   \n",
       "2   electricity_20        time  2012-01-01 01:00:00  2014-12-18 00:00:00   \n",
       "3       synth_data        time                    0                99999   \n",
       "4   electricity_20        time  2012-01-01 01:00:00  2013-04-29 09:00:00   \n",
       "5       mixturets2        time                    0               100000   \n",
       "\n",
       "  agg_interval  uncertainty_quantification  \n",
       "0      1 units                        True  \n",
       "1     00:00:05                        True  \n",
       "2     01:00:00                        True  \n",
       "3      1 units                        True  \n",
       "4     01:00:00                        True  \n",
       "5      1 units                        True  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = psycopg2.connect(conn_string)\n",
    "df = pd.read_sql_query(\"select * from list_pindices()\", conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looking at the row describing `pindex_20`, we can get information about which table, time columns and value columns the index is built on. \n",
    "\n",
    "- You can also see the first and last timestamp used to create the prediction index. \n",
    "\n",
    "\n",
    "With that, we are ready to use  **predict queries**! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Queries\n",
    "\n",
    "### Foracsting One hour ahead for the next  day\n",
    "\n",
    "Let's now use the prediction functionalities. \n",
    "\n",
    "Specifically, we will start by forecasting the first day for user `MT_020` and compare it visually with the test data.  Now we will produce the forecast for the next day using `predict_range` for the range `2014-12-18 01:00:00` to `2014-12-19  00:00:00`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "df = pd.read_sql_query('''select * from predict('electricity_20','MT_020','2014-12-18 01:00:00'\\\n",
    ",'2014-12-19  00:00:00','pindex_e20');''', conn)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot( df['prediction'], label = 'predictions')\n",
    "plt.plot( df['ub'], label = 'Upper Bound', alpha = 0.2)\n",
    "plt.plot(df['lb'], label = 'Lower Bound', alpha = 0.2)\n",
    "plt.plot(data_test['MT_020'].values[:len(df['prediction'])], label = 'Actual', alpha = 1.0)\n",
    "plt.title('Forecasting 1 day ahead')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast hourly data for all houses for the next seven days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will do a more qualitative test by forecasting the next seven days using a rolling window approach. Specifically, we will forecast the next seven days one day at a time for all 20 houses. Between forecasts, we will insert the already predicted data into the database.  We will then compute the error for all predictions vs. the actual readings.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day:  1\n",
      "starting from 2014-12-18 01:00:00 to  2014-12-19 00:00:00 \n",
      "predicting column:MT_001 for day 1 \n",
      "predicting column:MT_002 for day 1 \n",
      "predicting column:MT_003 for day 1 \n",
      "predicting column:MT_004 for day 1 \n",
      "predicting column:MT_005 for day 1 \n",
      "predicting column:MT_006 for day 1 \n",
      "predicting column:MT_007 for day 1 \n",
      "predicting column:MT_008 for day 1 \n",
      "predicting column:MT_009 for day 1 \n",
      "predicting column:MT_010 for day 1 \n",
      "predicting column:MT_011 for day 1 \n",
      "predicting column:MT_012 for day 1 \n",
      "predicting column:MT_013 for day 1 \n",
      "predicting column:MT_014 for day 1 \n",
      "predicting column:MT_015 for day 1 \n",
      "predicting column:MT_016 for day 1 \n",
      "predicting column:MT_017 for day 1 \n",
      "predicting column:MT_018 for day 1 \n",
      "predicting column:MT_019 for day 1 \n"
     ]
    }
   ],
   "source": [
    "#initialise prediction array\n",
    "predictions = np.zeros((len(data_test.columns),24*7))\n",
    "ub = np.zeros((len(data_test.columns),24*7))\n",
    "lb = np.zeros((len(data_test.columns),24*7))\n",
    "actual = data_test.values[:24*7,:]\n",
    "# specify start time\n",
    "start_time = pd.Timestamp('2014-12-18 01:00:00')\n",
    "# predict for seven days\n",
    "days = 7\n",
    "for day in range(days):\n",
    "    print('day: ',day+1)\n",
    "    # get the final time stamp in the day\n",
    "    end_time = start_time + pd.Timedelta(hours=23)\n",
    "    # convert timestamps to string\n",
    "    start_str = start_time.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    end_str = end_time.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    print('starting from %s to  %s '%(start_str,end_str))\n",
    "    # predict for each house\n",
    "    for i, column in enumerate(data_test.columns):\n",
    "        print('predicting column:%s for day %s '%(column,day+1))\n",
    "        # execute forecast query\n",
    "        df_30 = pd.read_sql_query('''select * from predict('electricity_20','%s','%s'\\\n",
    "    ,'%s','pindex_e20');'''%(column, start_str, end_str), conn)\n",
    "        predictions[i,day*24:(day+1)*24] = df_30['prediction']\n",
    "        ub[i,day*24:(day+1)*24] = df_30['ub']\n",
    "        lb[i,day*24:(day+1)*24] = df_30['lb']\n",
    "    # insert the already predicted values into the database\n",
    "    df_insert = data_test.iloc[day*24:24*(day+1),:]\n",
    "    bulk_insert(df_insert, 'electricity_20', conn_string)\n",
    "    # update start_time\n",
    "    start_time = start_time + pd.Timedelta(hours=24)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the error using the metric Weighted Absolute Percent Error (WAPE) which is calculated as:\n",
    "\n",
    "$\\text{WAPE} = \\frac{\\sum_i \\sum_j |Y_{ij}-\\hat{Y}_{ij}|} {\\sum_i \\sum_j |Y_{ij}|} $\n",
    "\n",
    "Where:\n",
    "- $Y_{ij}$ is the actual observation for time series j at time i \n",
    "- $\\hat{Y}_{ij}$ is the predicted value for time series j at time i "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = actual[:,:]\n",
    "Y_h = predictions.T[:,:]\n",
    "wape = np.sum(np.abs(Y-Y_h))/np.sum(np.abs(Y))\n",
    "print ('Forecasting accuracy (WAPE):',wape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's inspect our forecasts visually for all the 20 time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "for i in range(20):\n",
    "    plt.figure()\n",
    "    plt.title('forecasting the next seven days for %s'%data_test.columns[i])\n",
    "    plt.plot(predictions[i,:24*7],label= 'predictions')\n",
    "    plt.plot(ub[i,:24*7],'--',label= 'upper bound', alpha = 0.1)\n",
    "    plt.plot(lb[i,:24*7],'--',label= 'lower bound')\n",
    "    plt.plot(actual[:24*7,i],label = 'actual')\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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