anomaly_detection_Facebook_Prophet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "anomaly_detection_Facebook_Prophet.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPVyd/dr8I2s/gPrCdkNK00",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janael-pinheiro/time_series/blob/master/anomaly_detection_Facebook_Prophet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0A4gfoMMcBE",
        "outputId": "9f2a4859-eeea-4a25-f245-b8b55bcbc654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "!pip3 -q install investpy fbprophet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.4MB 5.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 41.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.5MB 46.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 512kB 45.7MB/s \n",
            "\u001b[?25h  Building wheel for investpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IaNlnaD3Mk-c",
        "outputId": "0fd9efc5-9365-4188-a1aa-02fafa5bbecd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import investpy\n",
        "from datetime import date\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from fbprophet import Prophet\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zd76cPSoMq-x"
      },
      "source": [
        "sns.set_style(\"whitegrid\", {\"grid.style\":\"--\"})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69XjBwdlMuKu"
      },
      "source": [
        "years = 10\n",
        "stock_name = \"AAPL\"\n",
        "country = \"United States\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-6TgkbvMxHK"
      },
      "source": [
        "initial_date = \"{0}/{1}/{2}\".format(date.today().day, date.today().month, date.today().year-years)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-L-ehotMyzQ"
      },
      "source": [
        "final_date = date.today().strftime(\"%d/%m/%Y\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JH0EhcTrM0k1"
      },
      "source": [
        "prices = investpy.get_stock_historical_data(stock=stock_name, from_date=initial_date, to_date=final_date, country=country)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RsTCGKNM2oj",
        "outputId": "8ed5ff85-aeff-4933-ac98-ab77fe92a229",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "source": [
        "prices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Currency</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-10-18</th>\n",
              "      <td>11.37</td>\n",
              "      <td>11.39</td>\n",
              "      <td>11.22</td>\n",
              "      <td>11.36</td>\n",
              "      <td>1104458624</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-19</th>\n",
              "      <td>10.84</td>\n",
              "      <td>11.21</td>\n",
              "      <td>10.71</td>\n",
              "      <td>11.05</td>\n",
              "      <td>1232760576</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-20</th>\n",
              "      <td>11.04</td>\n",
              "      <td>11.22</td>\n",
              "      <td>10.96</td>\n",
              "      <td>11.09</td>\n",
              "      <td>721624832</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-21</th>\n",
              "      <td>11.16</td>\n",
              "      <td>11.24</td>\n",
              "      <td>10.96</td>\n",
              "      <td>11.05</td>\n",
              "      <td>551457856</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-22</th>\n",
              "      <td>11.04</td>\n",
              "      <td>11.07</td>\n",
              "      <td>10.94</td>\n",
              "      <td>10.98</td>\n",
              "      <td>372775808</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-12</th>\n",
              "      <td>120.02</td>\n",
              "      <td>125.16</td>\n",
              "      <td>119.36</td>\n",
              "      <td>124.40</td>\n",
              "      <td>240226768</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-13</th>\n",
              "      <td>125.32</td>\n",
              "      <td>125.32</td>\n",
              "      <td>119.89</td>\n",
              "      <td>121.10</td>\n",
              "      <td>262330448</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-14</th>\n",
              "      <td>120.92</td>\n",
              "      <td>122.99</td>\n",
              "      <td>119.63</td>\n",
              "      <td>121.19</td>\n",
              "      <td>151062304</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-15</th>\n",
              "      <td>119.13</td>\n",
              "      <td>121.19</td>\n",
              "      <td>119.10</td>\n",
              "      <td>120.71</td>\n",
              "      <td>112559216</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-16</th>\n",
              "      <td>121.39</td>\n",
              "      <td>121.51</td>\n",
              "      <td>119.49</td>\n",
              "      <td>119.83</td>\n",
              "      <td>56831328</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2519 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open    High     Low   Close      Volume Currency\n",
              "Date                                                           \n",
              "2010-10-18   11.37   11.39   11.22   11.36  1104458624      USD\n",
              "2010-10-19   10.84   11.21   10.71   11.05  1232760576      USD\n",
              "2010-10-20   11.04   11.22   10.96   11.09   721624832      USD\n",
              "2010-10-21   11.16   11.24   10.96   11.05   551457856      USD\n",
              "2010-10-22   11.04   11.07   10.94   10.98   372775808      USD\n",
              "...            ...     ...     ...     ...         ...      ...\n",
              "2020-10-12  120.02  125.16  119.36  124.40   240226768      USD\n",
              "2020-10-13  125.32  125.32  119.89  121.10   262330448      USD\n",
              "2020-10-14  120.92  122.99  119.63  121.19   151062304      USD\n",
              "2020-10-15  119.13  121.19  119.10  120.71   112559216      USD\n",
              "2020-10-16  121.39  121.51  119.49  119.83    56831328      USD\n",
              "\n",
              "[2519 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diT8JWTwM4X1",
        "outputId": "7ea3094f-10b9-4c41-f0bf-9305b7ad106b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.lineplot(data=prices[\"Close\"])\n",
        "plt.xlabel(\"Day\")\n",
        "plt.ylabel(\"Price (US$)\")\n",
        "plt.xlim(pd.Timestamp(prices.index.min()),pd.Timestamp(prices.index.max()))\n",
        "plt.grid(True)\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKaf-4hcM70k"
      },
      "source": [
        "close_prices = prices[\"Close\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMEDx4ldNKY0"
      },
      "source": [
        "<h1>Anomaly detection</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQmNLvxbNEwl"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df[\"ds\"] = close_prices.index.values\n",
        "df[\"y\"] = close_prices.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuGL7yH4Nhkj"
      },
      "source": [
        "trainedModel = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True, seasonality_mode=\"additive\", interval_width=0.95)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yABlHFNSNmTv",
        "outputId": "8ed83ff4-1236-4696-9331-3767d6aec9d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "trainedModel.fit(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f91f28c49e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDbtPuFCNoNF"
      },
      "source": [
        "predictions = trainedModel.predict(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hriJ3HemNq8p",
        "outputId": "97f9e938-c3c1-4162-e076-bef15ac97ff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "predictions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-10-18</td>\n",
              "      <td>10.646379</td>\n",
              "      <td>1.677084</td>\n",
              "      <td>23.142544</td>\n",
              "      <td>10.646379</td>\n",
              "      <td>10.646379</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>2.183373</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.829751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-10-19</td>\n",
              "      <td>10.658220</td>\n",
              "      <td>2.133504</td>\n",
              "      <td>23.346775</td>\n",
              "      <td>10.658220</td>\n",
              "      <td>10.658220</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>2.082744</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.740964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-10-20</td>\n",
              "      <td>10.670062</td>\n",
              "      <td>2.581338</td>\n",
              "      <td>24.815342</td>\n",
              "      <td>10.670062</td>\n",
              "      <td>10.670062</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>1.980663</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.650725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-10-21</td>\n",
              "      <td>10.681904</td>\n",
              "      <td>0.958527</td>\n",
              "      <td>23.860299</td>\n",
              "      <td>10.681904</td>\n",
              "      <td>10.681904</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>1.878384</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.560288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-10-22</td>\n",
              "      <td>10.693746</td>\n",
              "      <td>1.690953</td>\n",
              "      <td>22.186763</td>\n",
              "      <td>10.693746</td>\n",
              "      <td>10.693746</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>1.777089</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.470835</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ds      trend  ...  multiplicative_terms_upper       yhat\n",
              "0 2010-10-18  10.646379  ...                         0.0  12.829751\n",
              "1 2010-10-19  10.658220  ...                         0.0  12.740964\n",
              "2 2010-10-20  10.670062  ...                         0.0  12.650725\n",
              "3 2010-10-21  10.681904  ...                         0.0  12.560288\n",
              "4 2010-10-22  10.693746  ...                         0.0  12.470835\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urna3yrRNsmI"
      },
      "source": [
        "predictions = predictions[['ds', 'yhat_lower', 'yhat_upper', 'yhat']]\n",
        "predictions[\"ytrue\"] = close_prices.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ln3ig1dlNywT"
      },
      "source": [
        "predictions['anomaly'] = np.where((predictions['ytrue'] < predictions['yhat_lower']) | (predictions['ytrue'] > predictions['yhat_upper']), True, False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCeCgKUUPoMD"
      },
      "source": [
        "<h1>Shows anomalies</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02LpG4HrN1M0"
      },
      "source": [
        "def createLayout(predictions):\n",
        "\n",
        "    layout = {'height': 420,\n",
        "        'width':950,\n",
        "        'plot_bgcolor': 'white',\n",
        "          'paper_bgcolor': 'white',\n",
        "          'margin': {'t':10, 'b':10, 'l':10, 'r':10, 'pad':0},\n",
        "          'yaxis': {'showgrid': True,\n",
        "                    'zeroline': True,\n",
        "                    'mirror': True,\n",
        "                    'color': '#737373',\n",
        "                    'linecolor': '#d9d9d9',\n",
        "                    'gridcolor': '#d9d9d9',\n",
        "                    'tickformat': '.0f'},\n",
        "          'xaxis': {'range':[predictions['ds'].min(), predictions['ds'].max()],\n",
        "                    'autorange': False,\n",
        "                    'showgrid': True,\n",
        "                    'zeroline': True,\n",
        "                    'mirror': True,\n",
        "                    'color': '#737373',\n",
        "                    'linecolor': '#d9d9d9',\n",
        "                    'gridcolor': '#d9d9d9',\n",
        "                    'type': 'date',\n",
        "                    'tickformat': '%d %b %y %H:%M',\n",
        "                    'tickangle': 0,\n",
        "                    'nticks': 5}}\n",
        "    return layout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AePRzWeUOAt_"
      },
      "source": [
        "def plotAnomalies(predictions):\n",
        "\n",
        "    data = []\n",
        "    data.append(go.Scatter(x=predictions.query('anomaly == False')['ds'],\n",
        "                        y=predictions.query('anomaly == False')['ytrue'],\n",
        "                        mode='markers',\n",
        "                        marker=dict(color='#000000', size=7),\n",
        "                        name='Price (US$)',\n",
        "                        hovertemplate='<b>Price (US$)</b><br>'\n",
        "                        '<b>Datetime:</b> %{x|%d %b %Y %H:%M:%S}<br>'\n",
        "                        '<b>Price (US$):</b> %{y: .2f}<extra></extra>'))\n",
        "\n",
        "    data.append(go.Scatter(x=predictions.query('anomaly == True')['ds'],\n",
        "                        y=predictions.query('anomaly == True')['ytrue'],\n",
        "                        mode='markers',\n",
        "                        marker=dict(color='#e60000', size=7),\n",
        "                        name='Anomalies',\n",
        "                        hovertemplate='<b>Anomalies</b><br>'\n",
        "                        '<b>Datetime:</b> %{x|%d %b %Y %H:%M:%S}<br>'\n",
        "                        '<b>Price (US$):</b> %{y: .2f}<extra></extra>'))\n",
        "\n",
        "    data.append(go.Scatter(x=predictions['ds'], y=predictions[\"yhat_lower\"], fill=\"tonexty\", mode='lines', name=\"Lower threshold\", line_color=\"#00b300\"))\n",
        "    data.append(go.Scatter(x=predictions['ds'], y=predictions[\"yhat_upper\"], fill=\"tonexty\", mode='lines', name=\"Upper threshold\", line_color=\"#00b300\"))\n",
        "\n",
        "    fig = go.Figure(data=data, layout=createLayout(predictions))\n",
        "    return fig"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2rpA6bZNQD1",
        "outputId": "616e3eab-e042-438b-c909-4e7ce8d31ecf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "plotAnomalies(predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
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