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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "# In-Sample Evaluation and Out-of-Sample Evaluation"
+      ],
+      "metadata": {}
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In-sample: data is what you have.       \n",
+        "Out-of-sample: is the data you do not have and want to forecast or estimate.  \n",
+        "\n",
+        "A way to numerically determine how good the model fits on dataset.\n",
+        "\n",
+        "\n",
+        "\n",
+        "Two important measures to determine the fit of a model:     \n",
+        "Mean Squared Error (MSE)     \n",
+        "R-squared (R^2)"
+      ],
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# fix_yahoo_finance is used to fetch data \n",
+        "import fix_yahoo_finance as yf\n",
+        "yf.pdr_override()"
+      ],
+      "outputs": [],
+      "execution_count": 1,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# input\n",
+        "symbol = 'AMD'\n",
+        "start = '2014-01-01'\n",
+        "end = '2018-08-27'\n",
+        "\n",
+        "# Read data \n",
+        "dataset = yf.download(symbol,start,end)\n",
+        "\n",
+        "# View columns \n",
+        "dataset.head()"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[*********************100%***********************]  1 of 1 downloaded\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "execution_count": 2,
+          "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>Adj Close</th>\n",
+              "      <th>Volume</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>2014-01-02</th>\n",
+              "      <td>3.85</td>\n",
+              "      <td>3.98</td>\n",
+              "      <td>3.84</td>\n",
+              "      <td>3.95</td>\n",
+              "      <td>3.95</td>\n",
+              "      <td>20548400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-03</th>\n",
+              "      <td>3.98</td>\n",
+              "      <td>4.00</td>\n",
+              "      <td>3.88</td>\n",
+              "      <td>4.00</td>\n",
+              "      <td>4.00</td>\n",
+              "      <td>22887200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-06</th>\n",
+              "      <td>4.01</td>\n",
+              "      <td>4.18</td>\n",
+              "      <td>3.99</td>\n",
+              "      <td>4.13</td>\n",
+              "      <td>4.13</td>\n",
+              "      <td>42398300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-07</th>\n",
+              "      <td>4.19</td>\n",
+              "      <td>4.25</td>\n",
+              "      <td>4.11</td>\n",
+              "      <td>4.18</td>\n",
+              "      <td>4.18</td>\n",
+              "      <td>42932100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-08</th>\n",
+              "      <td>4.23</td>\n",
+              "      <td>4.26</td>\n",
+              "      <td>4.14</td>\n",
+              "      <td>4.18</td>\n",
+              "      <td>4.18</td>\n",
+              "      <td>30678700</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Open  High   Low  Close  Adj Close    Volume\n",
+              "Date                                                    \n",
+              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
+              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
+              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
+              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
+              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 2,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset.shape"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 3,
+          "data": {
+            "text/plain": [
+              "(1172, 6)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 3,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = np.array(dataset['Open']).reshape(1172,-1)\n",
+        "Y = np.array(dataset['Adj Close']).reshape(1172,-1)"
+      ],
+      "outputs": [],
+      "execution_count": 4,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "lm = LinearRegression()\n",
+        "\n",
+        "lm.fit(X, Y)\n",
+        "# Find the R^2\n",
+        "lm.score(X, Y)"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 5,
+          "data": {
+            "text/plain": [
+              "0.9976829341182026"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 5,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Yhat = lm.predict(X)\n",
+        "Yhat[0:4]"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 6,
+          "data": {
+            "text/plain": [
+              "array([[3.85101685],\n",
+              "       [3.98104974],\n",
+              "       [4.01105733],\n",
+              "       [4.19110287]])"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 6,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "# mean_squared_error(Y_true, Y_predict)\n",
+        "mean_squared_error(dataset['Adj Close'],Yhat)"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 7,
+          "data": {
+            "text/plain": [
+              "0.055301235619033565"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 7,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "R-squared (R^2)\n",
+        "\n",
+        "The coefficient of Determination or R-squared.      \n",
+        "Measurment to defined how close the data fit the regression line.       \n",
+        "R-squared is the percentage of variation of the traget variable (Y) that is explained by the linear model.      \n",
+        "Comparing a regression model to a simple model is the mean of the data points.      \n",
+        "\n",
+        "R^2 = (1 - (MSE of regression line/MSE of the average of the data))\n",
+        "\n",
+        "Coefficient of Determination (R^2)      \n",
+        "The blue line represents the regression line.\n",
+        "\n",
+        "R-squared = Explained variation / Total variation\n",
+        "\n",
+        "R-squared is always between 0 and 100%:\n",
+        "\n",
+        "MSE is close to zero is good fit because the numerator is small and error is small. "
+      ],
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lm.score(X,Y) # R^2 (coefficient of determination) regression   "
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 8,
+          "data": {
+            "text/plain": [
+              "0.9976829341182026"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 8,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = dataset.drop(['Open','High','Low','Close','Volume'], axis=1)"
+      ],
+      "outputs": [],
+      "execution_count": 9,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 10,
+          "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>Adj Close</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Date</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2014-01-02</th>\n",
+              "      <td>3.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-03</th>\n",
+              "      <td>4.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-06</th>\n",
+              "      <td>4.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-07</th>\n",
+              "      <td>4.18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014-01-08</th>\n",
+              "      <td>4.18</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Adj Close\n",
+              "Date                 \n",
+              "2014-01-02       3.95\n",
+              "2014-01-03       4.00\n",
+              "2014-01-06       4.13\n",
+              "2014-01-07       4.18\n",
+              "2014-01-08       4.18"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 10,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(df)"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 11,
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x2527ce3fda0>]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": [
+              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\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 11,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Out-of-Sample Data \n",
+        "\n",
+        "Create new data (forecast)"
+      ],
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from fbprophet import Prophet"
+      ],
+      "outputs": [],
+      "execution_count": 12,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = dataset.reset_index()\n",
+        "data = data.drop(['Open','High','Low','Close','Volume'], axis=1)\n",
+        "new_df = data.rename(columns={'Date':'ds', 'Adj Close':'y'})\n",
+        "new_df.head()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 22,
+          "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>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2014-01-02</td>\n",
+              "      <td>3.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2014-01-03</td>\n",
+              "      <td>4.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2014-01-06</td>\n",
+              "      <td>4.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2014-01-07</td>\n",
+              "      <td>4.18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2014-01-08</td>\n",
+              "      <td>4.18</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          ds     y\n",
+              "0 2014-01-02  3.95\n",
+              "1 2014-01-03  4.00\n",
+              "2 2014-01-06  4.13\n",
+              "3 2014-01-07  4.18\n",
+              "4 2014-01-08  4.18"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 22,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Python\n",
+        "m = Prophet(daily_seasonality=True)\n",
+        "m.fit(new_df)"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 26,
+          "data": {
+            "text/plain": [
+              "<fbprophet.forecaster.Prophet at 0x2527e9feef0>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 26,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Out_of_Sample = m.make_future_dataframe(periods=365)\n",
+        "Out_of_Sample.head()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 27,
+          "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",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2014-01-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2014-01-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2014-01-06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2014-01-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2014-01-08</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          ds\n",
+              "0 2014-01-02\n",
+              "1 2014-01-03\n",
+              "2 2014-01-06\n",
+              "3 2014-01-07\n",
+              "4 2014-01-08"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 27,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Out_of_Sample.tail()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 28,
+          "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",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1532</th>\n",
+              "      <td>2019-08-23</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1533</th>\n",
+              "      <td>2019-08-24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1534</th>\n",
+              "      <td>2019-08-25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1535</th>\n",
+              "      <td>2019-08-26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1536</th>\n",
+              "      <td>2019-08-27</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             ds\n",
+              "1532 2019-08-23\n",
+              "1533 2019-08-24\n",
+              "1534 2019-08-25\n",
+              "1535 2019-08-26\n",
+              "1536 2019-08-27"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 28,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "forecast = m.predict(Out_of_Sample)\n",
+        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "execution_count": 30,
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ds</th>\n",
+              "      <th>yhat</th>\n",
+              "      <th>yhat_lower</th>\n",
+              "      <th>yhat_upper</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1532</th>\n",
+              "      <td>2019-08-23</td>\n",
+              "      <td>22.208154</td>\n",
+              "      <td>16.870245</td>\n",
+              "      <td>28.318758</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1533</th>\n",
+              "      <td>2019-08-24</td>\n",
+              "      <td>22.089846</td>\n",
+              "      <td>16.581817</td>\n",
+              "      <td>28.007437</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1534</th>\n",
+              "      <td>2019-08-25</td>\n",
+              "      <td>22.078294</td>\n",
+              "      <td>16.562700</td>\n",
+              "      <td>28.063231</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1535</th>\n",
+              "      <td>2019-08-26</td>\n",
+              "      <td>22.232008</td>\n",
+              "      <td>16.780503</td>\n",
+              "      <td>28.273691</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1536</th>\n",
+              "      <td>2019-08-27</td>\n",
+              "      <td>22.153669</td>\n",
+              "      <td>16.880195</td>\n",
+              "      <td>28.122720</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             ds       yhat  yhat_lower  yhat_upper\n",
+              "1532 2019-08-23  22.208154   16.870245   28.318758\n",
+              "1533 2019-08-24  22.089846   16.581817   28.007437\n",
+              "1534 2019-08-25  22.078294   16.562700   28.063231\n",
+              "1535 2019-08-26  22.232008   16.780503   28.273691\n",
+              "1536 2019-08-27  22.153669   16.880195   28.122720"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 30,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig1 = m.plot(forecast)"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ],
+            "image/png": [
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\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 31,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig2 = m.plot_components(forecast)"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 648x864 with 4 Axes>"
+            ],
+            "image/png": [
+              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\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 32,
+      "metadata": {
+        "collapsed": false,
+        "outputHidden": false,
+        "inputHidden": false
+      }
+    }
+  ],
+  "metadata": {
+    "kernel_info": {
+      "name": "python3"
+    },
+    "language_info": {
+      "file_extension": ".py",
+      "pygments_lexer": "ipython3",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "version": "3.5.5",
+      "mimetype": "text/x-python"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "language": "python",
+      "display_name": "Python 3"
+    },
+    "nteract": {
+      "version": "0.14.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
\ No newline at end of file