anomaly-detection-using-facebook-s-prophet.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "This code block contains import statements and setup."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "import altair as alt\n",
    "alt.renderers.enable('notebook')\n",
    "print(os.listdir(\"../input\"))\n",
    "from IPython.display import HTML\n",
    "\n",
    "\n",
    "# The below is great for working but if you publish it, no charts show up.\n",
    "# The workaround in the next cell deals with this.\n",
    "#alt.renderers.enable('notebook')\n",
    "\n",
    "HTML(\"This code block contains import statements and setup.\")\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "requirejs.config({\n",
       "    baseUrl: 'https://cdn.jsdelivr.net/npm/',\n",
       "    paths: {\"vega\": \"https://cdn.jsdelivr.net/npm/vega@v3.3.1?noext\", \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\", \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@v2.6.0?noext\", \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@3?noext\"}\n",
       "});\n",
       "</script>This code block sets up embedded rendering in HTML output and<br/>provides the function `render(chart, id='vega-chart')` for use below."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Dont worry about the code in this block. This is just the setup for showing Altair graphs in Kaggle Notebooks\n",
    "\n",
    "\n",
    "from  altair.vega import v3\n",
    "import json\n",
    "from IPython.display import HTML\n",
    "\n",
    "\n",
    "vega_url = 'https://cdn.jsdelivr.net/npm/vega@' + v3.SCHEMA_VERSION\n",
    "vega_lib_url = 'https://cdn.jsdelivr.net/npm/vega-lib'\n",
    "vega_lite_url = 'https://cdn.jsdelivr.net/npm/vega-lite@' + alt.SCHEMA_VERSION\n",
    "vega_embed_url = 'https://cdn.jsdelivr.net/npm/vega-embed@3'\n",
    "noext = \"?noext\"\n",
    "\n",
    "paths = {\n",
    "    'vega': vega_url + noext,\n",
    "    'vega-lib': vega_lib_url + noext,\n",
    "    'vega-lite': vega_lite_url + noext,\n",
    "    'vega-embed': vega_embed_url + noext\n",
    "}\n",
    "\n",
    "workaround = \"\"\"\n",
    "requirejs.config({{\n",
    "    baseUrl: 'https://cdn.jsdelivr.net/npm/',\n",
    "    paths: {}\n",
    "}});\n",
    "\"\"\"\n",
    "\n",
    "def add_autoincrement(render_func):\n",
    "    # Keep track of unique <div/> IDs\n",
    "    cache = {}\n",
    "    def wrapped(chart, id=\"vega-chart\", autoincrement=True):\n",
    "        if autoincrement:\n",
    "            if id in cache:\n",
    "                counter = 1 + cache[id]\n",
    "                cache[id] = counter\n",
    "            else:\n",
    "                cache[id] = 0\n",
    "            actual_id = id if cache[id] == 0 else id + '-' + str(cache[id])\n",
    "        else:\n",
    "            if id not in cache:\n",
    "                cache[id] = 0\n",
    "            actual_id = id\n",
    "        return render_func(chart, id=actual_id)\n",
    "    # Cache will stay outside and \n",
    "    return wrapped\n",
    "            \n",
    "@add_autoincrement\n",
    "def render(chart, id=\"vega-chart\"):\n",
    "    chart_str = \"\"\"\n",
    "    <div id=\"{id}\"></div><script>\n",
    "    require([\"vega-embed\"], function(vg_embed) {{\n",
    "        const spec = {chart};     \n",
    "        vg_embed(\"#{id}\", spec, {{defaultStyle: true}}).catch(console.warn);\n",
    "        console.log(\"anything?\");\n",
    "    }});\n",
    "    console.log(\"really...anything?\");\n",
    "    </script>\n",
    "    \"\"\"\n",
    "    return HTML(\n",
    "        chart_str.format(\n",
    "            id=id,\n",
    "            chart=json.dumps(chart) if isinstance(chart, dict) else chart.to_json(indent=None)\n",
    "        )\n",
    "    )\n",
    "\n",
    "HTML(\"\".join((\n",
    "    \"<script>\",\n",
    "    workaround.format(json.dumps(paths)),\n",
    "    \"</script>\",\n",
    "    \"This code block sets up embedded rendering in HTML output and<br/>\",\n",
    "    \"provides the function `render(chart, id='vega-chart')` for use below.\"\n",
    ")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "#!pip install fbprophet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing in background, pid 49.\r\n",
      "Output will be written to ‘wget-log’.\r\n"
     ]
    }
   ],
   "source": [
    "from fbprophet import Prophet\n",
    "\n",
    "!mkdir -p dataset\n",
    "!wget -c -b http://www-personal.umich.edu/~mejn/cp/data/sunspots.txt -P dataset\n",
    "data = np.loadtxt(\"dataset/sunspots.txt\", float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sunspots.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls dataset/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting data to Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Months</th>\n",
       "      <th>SunSpots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>1227.0</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>1228.0</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>1229.0</td>\n",
       "      <td>63.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1230.0</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>1231.0</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>1232.0</td>\n",
       "      <td>67.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>1233.0</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>1234.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>1236.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Months  SunSpots\n",
       "1227  1227.0      56.5\n",
       "1228  1228.0      62.6\n",
       "1229  1229.0      63.2\n",
       "1230  1230.0      36.1\n",
       "1231  1231.0      57.4\n",
       "1232  1232.0      67.9\n",
       "1233  1233.0      62.5\n",
       "1234  1234.0      51.0\n",
       "1235  1235.0      71.4\n",
       "1236  1236.0      68.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the data as a table\n",
    "data_as_frame = pd.DataFrame(data, columns=['Months', 'SunSpots'])\n",
    "data_as_frame.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_as_frame['ds']=data_as_frame['Months'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Months</th>\n",
       "      <th>SunSpots</th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Months  SunSpots  ds\n",
       "0     0.0      58.0   0\n",
       "1     1.0      62.6   1\n",
       "2     2.0      70.0   2\n",
       "3     3.0      55.7   3\n",
       "4     4.0      85.0   4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_as_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting the months column in format acceptable for Prophet, starting from 1960 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    " data_as_frame['time_stamp']=data_as_frame.apply(lambda x:(pd.Timestamp('1960-01-01')+pd.DateOffset(months = int(x['ds']))),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning the df, we only need two columns date time and the data\n",
    "clean_df=data_as_frame.drop(['Months','ds'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SunSpots</th>\n",
       "      <th>time_stamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1960-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.6</td>\n",
       "      <td>1960-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1960-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.7</td>\n",
       "      <td>1960-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85.0</td>\n",
       "      <td>1960-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SunSpots time_stamp\n",
       "0      58.0 1960-01-01\n",
       "1      62.6 1960-02-01\n",
       "2      70.0 1960-03-01\n",
       "3      55.7 1960-04-01\n",
       "4      85.0 1960-05-01"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets view the data in graphical format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div id=\"vega-chart\"></div><script>\n",
       "    require([\"vega-embed\"], function(vg_embed) {\n",
       "        const spec = {\"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"config\": {\"title\": {\"fontSize\": 20}, \"view\": {\"height\": 300, \"width\": 400}}, \"data\": {\"name\": \"data-967f4104958f217e35b80935bc72120c\"}, \"datasets\": {\"data-967f4104958f217e35b80935bc72120c\": [{\"SunSpots\": 58.0, \"time_stamp\": \"1960-01-01T00:00:00\"}, {\"SunSpots\": 62.6, \"time_stamp\": \"1960-02-01T00:00:00\"}, {\"SunSpots\": 70.0, \"time_stamp\": \"1960-03-01T00:00:00\"}, {\"SunSpots\": 55.7, \"time_stamp\": \"1960-04-01T00:00:00\"}, {\"SunSpots\": 85.0, \"time_stamp\": \"1960-05-01T00:00:00\"}, {\"SunSpots\": 83.5, \"time_stamp\": \"1960-06-01T00:00:00\"}, {\"SunSpots\": 94.8, \"time_stamp\": \"1960-07-01T00:00:00\"}, {\"SunSpots\": 66.3, \"time_stamp\": \"1960-08-01T00:00:00\"}, {\"SunSpots\": 75.9, \"time_stamp\": \"1960-09-01T00:00:00\"}, {\"SunSpots\": 75.5, \"time_stamp\": \"1960-10-01T00:00:00\"}, {\"SunSpots\": 158.6, \"time_stamp\": \"1960-11-01T00:00:00\"}, {\"SunSpots\": 85.2, \"time_stamp\": \"1960-12-01T00:00:00\"}, {\"SunSpots\": 73.3, \"time_stamp\": \"1961-01-01T00:00:00\"}, {\"SunSpots\": 75.9, \"time_stamp\": \"1961-02-01T00:00:00\"}, {\"SunSpots\": 89.2, \"time_stamp\": \"1961-03-01T00:00:00\"}, {\"SunSpots\": 88.3, \"time_stamp\": \"1961-04-01T00:00:00\"}, {\"SunSpots\": 90.0, \"time_stamp\": \"1961-05-01T00:00:00\"}, {\"SunSpots\": 100.0, \"time_stamp\": \"1961-06-01T00:00:00\"}, {\"SunSpots\": 85.4, \"time_stamp\": \"1961-07-01T00:00:00\"}, {\"SunSpots\": 103.0, \"time_stamp\": \"1961-08-01T00:00:00\"}, {\"SunSpots\": 91.2, \"time_stamp\": \"1961-09-01T00:00:00\"}, {\"SunSpots\": 65.7, \"time_stamp\": \"1961-10-01T00:00:00\"}, {\"SunSpots\": 63.3, \"time_stamp\": \"1961-11-01T00:00:00\"}, {\"SunSpots\": 75.4, \"time_stamp\": \"1961-12-01T00:00:00\"}, {\"SunSpots\": 70.0, \"time_stamp\": \"1962-01-01T00:00:00\"}, {\"SunSpots\": 43.5, \"time_stamp\": \"1962-02-01T00:00:00\"}, {\"SunSpots\": 45.3, \"time_stamp\": \"1962-03-01T00:00:00\"}, {\"SunSpots\": 56.4, \"time_stamp\": \"1962-04-01T00:00:00\"}, {\"SunSpots\": 60.7, \"time_stamp\": \"1962-05-01T00:00:00\"}, {\"SunSpots\": 50.7, \"time_stamp\": \"1962-06-01T00:00:00\"}, {\"SunSpots\": 66.3, \"time_stamp\": \"1962-07-01T00:00:00\"}, {\"SunSpots\": 59.8, \"time_stamp\": \"1962-08-01T00:00:00\"}, {\"SunSpots\": 23.5, \"time_stamp\": \"1962-09-01T00:00:00\"}, {\"SunSpots\": 23.2, \"time_stamp\": \"1962-10-01T00:00:00\"}, {\"SunSpots\": 28.5, \"time_stamp\": \"1962-11-01T00:00:00\"}, {\"SunSpots\": 44.0, \"time_stamp\": \"1962-12-01T00:00:00\"}, {\"SunSpots\": 35.0, \"time_stamp\": \"1963-01-01T00:00:00\"}, {\"SunSpots\": 50.0, \"time_stamp\": \"1963-02-01T00:00:00\"}, {\"SunSpots\": 71.0, \"time_stamp\": \"1963-03-01T00:00:00\"}, {\"SunSpots\": 59.3, \"time_stamp\": \"1963-04-01T00:00:00\"}, {\"SunSpots\": 59.7, \"time_stamp\": \"1963-05-01T00:00:00\"}, {\"SunSpots\": 39.6, \"time_stamp\": \"1963-06-01T00:00:00\"}, 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\"time_stamp\": \"2056-10-01T00:00:00\"}, {\"SunSpots\": 39.4, \"time_stamp\": \"2056-11-01T00:00:00\"}, {\"SunSpots\": 59.7, \"time_stamp\": \"2056-12-01T00:00:00\"}, {\"SunSpots\": 38.7, \"time_stamp\": \"2057-01-01T00:00:00\"}, {\"SunSpots\": 51.0, \"time_stamp\": \"2057-02-01T00:00:00\"}, {\"SunSpots\": 63.9, \"time_stamp\": \"2057-03-01T00:00:00\"}, {\"SunSpots\": 69.3, \"time_stamp\": \"2057-04-01T00:00:00\"}, {\"SunSpots\": 60.0, \"time_stamp\": \"2057-05-01T00:00:00\"}, {\"SunSpots\": 65.1, \"time_stamp\": \"2057-06-01T00:00:00\"}, {\"SunSpots\": 46.5, \"time_stamp\": \"2057-07-01T00:00:00\"}, {\"SunSpots\": 54.8, \"time_stamp\": \"2057-08-01T00:00:00\"}, {\"SunSpots\": 107.1, \"time_stamp\": \"2057-09-01T00:00:00\"}, {\"SunSpots\": 55.9, \"time_stamp\": \"2057-10-01T00:00:00\"}, {\"SunSpots\": 60.4, \"time_stamp\": \"2057-11-01T00:00:00\"}, {\"SunSpots\": 65.5, \"time_stamp\": \"2057-12-01T00:00:00\"}, {\"SunSpots\": 62.6, \"time_stamp\": \"2058-01-01T00:00:00\"}, {\"SunSpots\": 44.9, \"time_stamp\": \"2058-02-01T00:00:00\"}, {\"SunSpots\": 85.7, \"time_stamp\": \"2058-03-01T00:00:00\"}, {\"SunSpots\": 44.8, \"time_stamp\": \"2058-04-01T00:00:00\"}, {\"SunSpots\": 75.5, \"time_stamp\": \"2058-05-01T00:00:00\"}, {\"SunSpots\": 85.3, \"time_stamp\": \"2058-06-01T00:00:00\"}, {\"SunSpots\": 52.2, \"time_stamp\": \"2058-07-01T00:00:00\"}, {\"SunSpots\": 140.6, \"time_stamp\": \"2058-08-01T00:00:00\"}, {\"SunSpots\": 160.9, \"time_stamp\": \"2058-09-01T00:00:00\"}, {\"SunSpots\": 180.4, \"time_stamp\": \"2058-10-01T00:00:00\"}, {\"SunSpots\": 138.9, \"time_stamp\": \"2058-11-01T00:00:00\"}, {\"SunSpots\": 109.6, \"time_stamp\": \"2058-12-01T00:00:00\"}, {\"SunSpots\": 159.1, \"time_stamp\": \"2059-01-01T00:00:00\"}, {\"SunSpots\": 111.8, \"time_stamp\": \"2059-02-01T00:00:00\"}, {\"SunSpots\": 108.6, \"time_stamp\": \"2059-03-01T00:00:00\"}, {\"SunSpots\": 107.1, \"time_stamp\": \"2059-04-01T00:00:00\"}, {\"SunSpots\": 102.2, \"time_stamp\": \"2059-05-01T00:00:00\"}, {\"SunSpots\": 129.0, \"time_stamp\": \"2059-06-01T00:00:00\"}, {\"SunSpots\": 139.2, \"time_stamp\": \"2059-07-01T00:00:00\"}, {\"SunSpots\": 132.6, \"time_stamp\": \"2059-08-01T00:00:00\"}, {\"SunSpots\": 100.3, \"time_stamp\": \"2059-09-01T00:00:00\"}, {\"SunSpots\": 132.4, \"time_stamp\": \"2059-10-01T00:00:00\"}, {\"SunSpots\": 114.6, \"time_stamp\": \"2059-11-01T00:00:00\"}, {\"SunSpots\": 159.5, \"time_stamp\": \"2059-12-01T00:00:00\"}, {\"SunSpots\": 157.0, \"time_stamp\": \"2060-01-01T00:00:00\"}, {\"SunSpots\": 131.8, \"time_stamp\": \"2060-02-01T00:00:00\"}, {\"SunSpots\": 96.2, \"time_stamp\": \"2060-03-01T00:00:00\"}, {\"SunSpots\": 102.5, \"time_stamp\": \"2060-04-01T00:00:00\"}, {\"SunSpots\": 80.6, \"time_stamp\": \"2060-05-01T00:00:00\"}, {\"SunSpots\": 81.1, \"time_stamp\": \"2060-06-01T00:00:00\"}, {\"SunSpots\": 78.0, \"time_stamp\": \"2060-07-01T00:00:00\"}, {\"SunSpots\": 67.7, \"time_stamp\": \"2060-08-01T00:00:00\"}, {\"SunSpots\": 93.7, \"time_stamp\": \"2060-09-01T00:00:00\"}, {\"SunSpots\": 71.5, \"time_stamp\": \"2060-10-01T00:00:00\"}, {\"SunSpots\": 99.0, \"time_stamp\": \"2060-11-01T00:00:00\"}, {\"SunSpots\": 97.0, \"time_stamp\": \"2060-12-01T00:00:00\"}, {\"SunSpots\": 78.0, \"time_stamp\": \"2061-01-01T00:00:00\"}, {\"SunSpots\": 89.4, \"time_stamp\": \"2061-02-01T00:00:00\"}, {\"SunSpots\": 82.6, \"time_stamp\": \"2061-03-01T00:00:00\"}, {\"SunSpots\": 44.1, \"time_stamp\": \"2061-04-01T00:00:00\"}, {\"SunSpots\": 61.6, \"time_stamp\": \"2061-05-01T00:00:00\"}, {\"SunSpots\": 70.0, \"time_stamp\": \"2061-06-01T00:00:00\"}, {\"SunSpots\": 39.1, \"time_stamp\": \"2061-07-01T00:00:00\"}, {\"SunSpots\": 61.6, \"time_stamp\": \"2061-08-01T00:00:00\"}, {\"SunSpots\": 86.2, \"time_stamp\": \"2061-09-01T00:00:00\"}, {\"SunSpots\": 71.0, \"time_stamp\": \"2061-10-01T00:00:00\"}, {\"SunSpots\": 54.8, \"time_stamp\": \"2061-11-01T00:00:00\"}, {\"SunSpots\": 61.0, \"time_stamp\": \"2061-12-01T00:00:00\"}, {\"SunSpots\": 75.5, \"time_stamp\": \"2062-01-01T00:00:00\"}, {\"SunSpots\": 105.4, \"time_stamp\": \"2062-02-01T00:00:00\"}, {\"SunSpots\": 64.6, \"time_stamp\": \"2062-03-01T00:00:00\"}, {\"SunSpots\": 56.5, \"time_stamp\": \"2062-04-01T00:00:00\"}, {\"SunSpots\": 62.6, \"time_stamp\": \"2062-05-01T00:00:00\"}, {\"SunSpots\": 63.2, \"time_stamp\": \"2062-06-01T00:00:00\"}, {\"SunSpots\": 36.1, \"time_stamp\": \"2062-07-01T00:00:00\"}, {\"SunSpots\": 57.4, \"time_stamp\": \"2062-08-01T00:00:00\"}, {\"SunSpots\": 67.9, \"time_stamp\": \"2062-09-01T00:00:00\"}, {\"SunSpots\": 62.5, \"time_stamp\": \"2062-10-01T00:00:00\"}, {\"SunSpots\": 51.0, \"time_stamp\": \"2062-11-01T00:00:00\"}, {\"SunSpots\": 71.4, \"time_stamp\": \"2062-12-01T00:00:00\"}, {\"SunSpots\": 68.0, \"time_stamp\": \"2063-01-01T00:00:00\"}]}, \"encoding\": {\"tooltip\": [{\"field\": \"time_stamp\", \"timeUnit\": \"yearmonthdate\", \"type\": \"temporal\"}, {\"field\": \"SunSpots\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"time_stamp\", \"type\": \"temporal\"}, \"y\": {\"field\": \"SunSpots\", \"title\": \"Sunspots\", \"type\": \"quantitative\"}}, \"height\": 450, \"mark\": {\"color\": \"Orange\", \"opacity\": 0.8, \"size\": 15, \"type\": \"line\"}, \"selection\": {\"selector001\": {\"bind\": \"scales\", \"encodings\": [\"x\", \"y\"], \"type\": \"interval\"}}, \"title\": \"Sunspots based on Months\", \"width\": 900};     \n",
       "        vg_embed(\"#vega-chart\", spec, {defaultStyle: true}).catch(console.warn);\n",
       "        console.log(\"anything?\");\n",
       "    });\n",
       "    console.log(\"really...anything?\");\n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "render(alt.Chart(clean_df).mark_line(size=15, opacity=0.8, color = 'Orange').encode(\n",
    "        x='time_stamp:T',\n",
    "        y=alt.Y('SunSpots', title='Sunspots'),    \n",
    "        tooltip=['yearmonthdate(time_stamp)', 'SunSpots']\n",
    "    ).interactive().properties(width=900, height=450,title='Sunspots based on Months')\\\n",
    "              .configure_title(fontSize=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing data for modelling in Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Prophet requires two columns, one is ds (the date time) and y (variable to be forecasted)\n",
    "clean_df.columns = ['y', 'ds']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying Prophet plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_predict_model(dataframe, interval_width = 0.99, changepoint_range = 0.8):\n",
    "    m = Prophet(daily_seasonality = False, yearly_seasonality = False, weekly_seasonality = False,\n",
    "                seasonality_mode = 'multiplicative', \n",
    "                interval_width = interval_width,\n",
    "                changepoint_range = changepoint_range)\n",
    "    m = m.fit(dataframe)\n",
    "    \n",
    "    forecast = m.predict(dataframe)\n",
    "    forecast['fact'] = dataframe['y'].reset_index(drop = True)\n",
    "    print('Displaying Prophet plot')\n",
    "    fig1 = m.plot(forecast)\n",
    "    return forecast\n",
    "    \n",
    "pred = fit_predict_model(clean_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting Anomalies:\n",
    "* The light blue boundaries in the above graph are yhat_upper and yhat_lower.\n",
    "* If y value is greater than yhat_upper and less than yhat lower then it is an anomaly.\n",
    "* Also getting the importance of that anomaly based on its distance from yhat_upper and yhat_lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_anomalies(forecast):\n",
    "    forecasted = forecast[['ds','trend', 'yhat', 'yhat_lower', 'yhat_upper', 'fact']].copy()\n",
    "    #forecast['fact'] = df['y']\n",
    "\n",
    "    forecasted['anomaly'] = 0\n",
    "    forecasted.loc[forecasted['fact'] > forecasted['yhat_upper'], 'anomaly'] = 1\n",
    "    forecasted.loc[forecasted['fact'] < forecasted['yhat_lower'], 'anomaly'] = -1\n",
    "\n",
    "    #anomaly importances\n",
    "    forecasted['importance'] = 0\n",
    "    forecasted.loc[forecasted['anomaly'] ==1, 'importance'] = \\\n",
    "        (forecasted['fact'] - forecasted['yhat_upper'])/forecast['fact']\n",
    "    forecasted.loc[forecasted['anomaly'] ==-1, 'importance'] = \\\n",
    "        (forecasted['yhat_lower'] - forecasted['fact'])/forecast['fact']\n",
    "    \n",
    "    return forecasted\n",
    "\n",
    "pred = detect_anomalies(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>fact</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>43.108918</td>\n",
       "      <td>43.108918</td>\n",
       "      <td>-54.905745</td>\n",
       "      <td>133.131323</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-02-01</td>\n",
       "      <td>43.167194</td>\n",
       "      <td>43.167194</td>\n",
       "      <td>-48.619176</td>\n",
       "      <td>135.129137</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1960-03-01</td>\n",
       "      <td>43.221710</td>\n",
       "      <td>43.221710</td>\n",
       "      <td>-45.797521</td>\n",
       "      <td>135.426337</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960-04-01</td>\n",
       "      <td>43.279987</td>\n",
       "      <td>43.279987</td>\n",
       "      <td>-46.272528</td>\n",
       "      <td>130.575765</td>\n",
       "      <td>55.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960-05-01</td>\n",
       "      <td>43.336383</td>\n",
       "      <td>43.336383</td>\n",
       "      <td>-45.860989</td>\n",
       "      <td>139.898457</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      trend       yhat     ...      fact  anomaly  importance\n",
       "0 1960-01-01  43.108918  43.108918     ...      58.0        0         0.0\n",
       "1 1960-02-01  43.167194  43.167194     ...      62.6        0         0.0\n",
       "2 1960-03-01  43.221710  43.221710     ...      70.0        0         0.0\n",
       "3 1960-04-01  43.279987  43.279987     ...      55.7        0         0.0\n",
       "4 1960-05-01  43.336383  43.336383     ...      85.0        0         0.0\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the anomalies for a better view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div id=\"vega-chart-1\"></div><script>\n",
       "    require([\"vega-embed\"], function(vg_embed) {\n",
       "        const spec = {\"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"config\": {\"title\": {\"fontSize\": 20}, \"view\": {\"height\": 300, \"width\": 400}}, \"datasets\": {\"data-48eaa833aad2c4a6148b7190d113395e\": [{\"anomaly\": 0, \"ds\": \"1960-01-01T00:00:00\", \"fact\": 58.0, \"importance\": 0.0, \"trend\": 43.10891750258137, \"yhat\": 43.10891750258137, \"yhat_lower\": -54.90574499194275, \"yhat_upper\": 133.13132300902274}, {\"anomaly\": 0, \"ds\": \"1960-02-01T00:00:00\", \"fact\": 62.6, \"importance\": 0.0, \"trend\": 43.1671938244655, \"yhat\": 43.1671938244655, \"yhat_lower\": -48.619175554989205, \"yhat_upper\": 135.1291374661618}, {\"anomaly\": 0, \"ds\": \"1960-03-01T00:00:00\", \"fact\": 70.0, \"importance\": 0.0, \"trend\": 43.22171038364742, \"yhat\": 43.22171038364742, \"yhat_lower\": -45.79752117007113, \"yhat_upper\": 135.42633694555576}, {\"anomaly\": 0, \"ds\": \"1960-04-01T00:00:00\", \"fact\": 55.7, \"importance\": 0.0, \"trend\": 43.27998670553155, \"yhat\": 43.27998670553155, \"yhat_lower\": -46.27252754466062, \"yhat_upper\": 130.5757652809509}, {\"anomaly\": 0, \"ds\": \"1960-05-01T00:00:00\", \"fact\": 85.0, \"importance\": 0.0, \"trend\": 43.33638314606457, \"yhat\": 43.33638314606457, \"yhat_lower\": -45.86098887643784, \"yhat_upper\": 139.89845654845527}, {\"anomaly\": 0, \"ds\": \"1960-06-01T00:00:00\", \"fact\": 83.5, \"importance\": 0.0, \"trend\": 43.3946594679487, \"yhat\": 43.3946594679487, \"yhat_lower\": -46.96001124595743, \"yhat_upper\": 130.68793343248726}, {\"anomaly\": 0, \"ds\": \"1960-07-01T00:00:00\", \"fact\": 94.8, \"importance\": 0.0, \"trend\": 43.45105590848172, \"yhat\": 43.45105590848172, \"yhat_lower\": -41.4740971595004, \"yhat_upper\": 130.03099964889884}, {\"anomaly\": 0, \"ds\": \"1960-08-01T00:00:00\", \"fact\": 66.3, \"importance\": 0.0, \"trend\": 43.50933223036584, \"yhat\": 43.50933223036584, \"yhat_lower\": -44.42664130254032, \"yhat_upper\": 134.72863980967617}, {\"anomaly\": 0, \"ds\": \"1960-09-01T00:00:00\", \"fact\": 75.9, \"importance\": 0.0, \"trend\": 43.56760855224997, \"yhat\": 43.56760855224997, \"yhat_lower\": -45.03307673106076, \"yhat_upper\": 136.23511552356965}, {\"anomaly\": 0, \"ds\": \"1960-10-01T00:00:00\", \"fact\": 75.5, \"importance\": 0.0, \"trend\": 43.62400499278299, \"yhat\": 43.62400499278299, \"yhat_lower\": -41.16130520295547, \"yhat_upper\": 143.6565603422132}, {\"anomaly\": 1, \"ds\": \"1960-11-01T00:00:00\", \"fact\": 158.6, \"importance\": 0.17440984228616155, \"trend\": 43.68228131466712, \"yhat\": 43.68228131466712, \"yhat_lower\": -55.506261519162926, \"yhat_upper\": 130.93859901341477}, {\"anomaly\": 0, \"ds\": \"1960-12-01T00:00:00\", \"fact\": 85.2, \"importance\": 0.0, \"trend\": 43.73867775520014, \"yhat\": 43.73867775520014, \"yhat_lower\": -44.333195569316416, \"yhat_upper\": 144.07357270040544}, {\"anomaly\": 0, \"ds\": \"1961-01-01T00:00:00\", \"fact\": 73.3, \"importance\": 0.0, \"trend\": 43.796954077084266, \"yhat\": 43.796954077084266, \"yhat_lower\": -47.10370187980933, \"yhat_upper\": 126.91183524461898}, {\"anomaly\": 0, \"ds\": \"1961-02-01T00:00:00\", \"fact\": 75.9, \"importance\": 0.0, \"trend\": 43.855230398968395, \"yhat\": 43.855230398968395, \"yhat_lower\": -47.06663689501429, \"yhat_upper\": 132.9251789257567}, {\"anomaly\": 0, \"ds\": \"1961-03-01T00:00:00\", \"fact\": 89.2, \"importance\": 0.0, \"trend\": 43.90786707679922, \"yhat\": 43.90786707679922, \"yhat_lower\": -43.821137831624576, \"yhat_upper\": 138.34252120178758}, {\"anomaly\": 0, \"ds\": \"1961-04-01T00:00:00\", \"fact\": 88.3, \"importance\": 0.0, \"trend\": 43.96614339868334, \"yhat\": 43.96614339868334, \"yhat_lower\": -55.40446814266869, \"yhat_upper\": 143.58232396539998}, {\"anomaly\": 0, \"ds\": \"1961-05-01T00:00:00\", \"fact\": 90.0, \"importance\": 0.0, \"trend\": 44.022539839216364, \"yhat\": 44.022539839216364, \"yhat_lower\": -46.348185264185354, \"yhat_upper\": 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\"yhat_lower\": -11.96744821983558, \"yhat_upper\": 165.33727957018309}, {\"anomaly\": 0, \"ds\": \"2062-09-01T00:00:00\", \"fact\": 67.9, \"importance\": 0.0, \"trend\": 77.65112287142989, \"yhat\": 77.65112287142989, \"yhat_lower\": -12.401691770417454, \"yhat_upper\": 176.16252288822747}, {\"anomaly\": 0, \"ds\": \"2062-10-01T00:00:00\", \"fact\": 62.5, \"importance\": 0.0, \"trend\": 77.78745064958825, \"yhat\": 77.78745064958825, \"yhat_lower\": -10.69794562329669, \"yhat_upper\": 165.34096854967947}, {\"anomaly\": 0, \"ds\": \"2062-11-01T00:00:00\", \"fact\": 51.0, \"importance\": 0.0, \"trend\": 77.92832268701854, \"yhat\": 77.92832268701854, \"yhat_lower\": -4.658939457979118, \"yhat_upper\": 158.76884373284773}, {\"anomaly\": 0, \"ds\": \"2062-12-01T00:00:00\", \"fact\": 71.4, \"importance\": 0.0, \"trend\": 78.06465046517688, \"yhat\": 78.06465046517688, \"yhat_lower\": -8.951232677763128, \"yhat_upper\": 163.92368487684269}, {\"anomaly\": 0, \"ds\": \"2063-01-01T00:00:00\", \"fact\": 68.0, \"importance\": 0.0, \"trend\": 78.20552250260717, \"yhat\": 78.20552250260717, \"yhat_lower\": -14.796883772958168, \"yhat_upper\": 174.48125123347984}]}, \"height\": 450, \"layer\": [{\"data\": {\"name\": \"data-48eaa833aad2c4a6148b7190d113395e\"}, \"encoding\": {\"tooltip\": [{\"field\": \"ds\", \"timeUnit\": \"yearmonthdate\", \"type\": \"temporal\"}, {\"field\": \"fact\", \"type\": \"quantitative\"}, {\"field\": \"yhat_lower\", \"type\": \"quantitative\"}, {\"field\": \"yhat_upper\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"ds\", \"title\": \"date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"yhat_upper\", \"type\": \"quantitative\"}, \"y2\": {\"field\": \"yhat_lower\", \"type\": \"quantitative\"}}, \"mark\": {\"color\": \"#7FC97F\", \"interpolate\": \"basis\", \"type\": \"area\"}, \"selection\": {\"selector002\": {\"bind\": \"scales\", \"encodings\": [\"x\", \"y\"], \"type\": \"interval\"}}, \"title\": \"Anomaly Detection\"}, {\"data\": {\"name\": \"data-c5e84cd9d9b238c3508bd324e0e326ad\"}, \"encoding\": {\"tooltip\": [{\"field\": \"ds\", \"timeUnit\": \"yearmonthdate\", \"type\": \"temporal\"}, {\"field\": \"fact\", \"type\": \"quantitative\"}, {\"field\": \"yhat_lower\", \"type\": \"quantitative\"}, {\"field\": \"yhat_upper\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"ds\", \"type\": \"temporal\"}, \"y\": {\"field\": \"fact\", \"title\": \"Sunspots\", \"type\": \"quantitative\"}}, \"mark\": {\"color\": \"Black\", \"opacity\": 0.7, \"size\": 15, \"type\": \"circle\"}, \"selection\": {\"selector003\": {\"bind\": \"scales\", \"encodings\": [\"x\", \"y\"], \"type\": \"interval\"}}}, {\"data\": {\"name\": \"data-b8bb9bb3ce25829f3fcc53b5ed8eaff1\"}, \"encoding\": {\"size\": {\"field\": \"importance\", \"legend\": null, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"ds\", \"timeUnit\": \"yearmonthdate\", \"type\": \"temporal\"}, {\"field\": \"fact\", \"type\": \"quantitative\"}, {\"field\": \"yhat_lower\", \"type\": \"quantitative\"}, {\"field\": \"yhat_upper\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"ds\", \"type\": \"temporal\"}, \"y\": {\"field\": \"fact\", \"title\": \"Sunspots\", \"type\": \"quantitative\"}}, \"mark\": {\"color\": \"Red\", \"size\": 30, \"type\": \"circle\"}, \"selection\": {\"selector004\": {\"bind\": \"scales\", \"encodings\": [\"x\", \"y\"], \"type\": \"interval\"}}}], \"width\": 870};     \n",
       "        vg_embed(\"#vega-chart-1\", spec, {defaultStyle: true}).catch(console.warn);\n",
       "        console.log(\"anything?\");\n",
       "    });\n",
       "    console.log(\"really...anything?\");\n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_anomalies(forecasted):\n",
    "    interval = alt.Chart(forecasted).mark_area(interpolate=\"basis\", color = '#7FC97F').encode(\n",
    "    x=alt.X('ds:T',  title ='date'),\n",
    "    y='yhat_upper',\n",
    "    y2='yhat_lower',\n",
    "    tooltip=['yearmonthdate(ds)', 'fact', 'yhat_lower', 'yhat_upper']\n",
    "    ).interactive().properties(\n",
    "        title='Anomaly Detection'\n",
    "    )\n",
    "\n",
    "    fact = alt.Chart(forecasted[forecasted.anomaly==0]).mark_circle(size=15, opacity=0.7, color = 'Black').encode(\n",
    "        x='ds:T',\n",
    "        y=alt.Y('fact', title='Sunspots'),    \n",
    "        tooltip=['yearmonthdate(ds)', 'fact', 'yhat_lower', 'yhat_upper']\n",
    "    ).interactive()\n",
    "\n",
    "    anomalies = alt.Chart(forecasted[forecasted.anomaly!=0]).mark_circle(size=30, color = 'Red').encode(\n",
    "        x='ds:T',\n",
    "        y=alt.Y('fact', title='Sunspots'),    \n",
    "        tooltip=['yearmonthdate(ds)', 'fact', 'yhat_lower', 'yhat_upper'],\n",
    "        size = alt.Size( 'importance', legend=None)\n",
    "    ).interactive()\n",
    "\n",
    "    return render(alt.layer(interval, fact, anomalies)\\\n",
    "              .properties(width=870, height=450)\\\n",
    "              .configure_title(fontSize=20))\n",
    "              \n",
    "plot_anomalies(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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