Stocks_Dataset_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7YEi3C2_Dvji"
   },
   "source": [
    "# Stocks Dataset- Time Series Forecasting through RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data & Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75,
     "resources": {
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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "61LV0d0ZClKT",
    "outputId": "326e8668-0c86-4cb9-ed55-7b7bfaa1b632"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-f15da029-2576-42e0-9ae7-9170f8fa21ad\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-f15da029-2576-42e0-9ae7-9170f8fa21ad\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving DowJones.csv to DowJones.csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WO9tWW8qFFXf"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "Si5lrfheFF_r",
    "outputId": "6a575777-182b-4b86-ad26-fd7a8e878c3d"
   },
   "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>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977-09-02</td>\n",
       "      <td>872.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977-09-09</td>\n",
       "      <td>857.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977-09-16</td>\n",
       "      <td>856.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977-09-23</td>\n",
       "      <td>839.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977-09-30</td>\n",
       "      <td>847.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Value\n",
       "0  1977-09-02  872.31\n",
       "1  1977-09-09  857.04\n",
       "2  1977-09-16  856.81\n",
       "3  1977-09-23  839.14\n",
       "4  1977-09-30  847.11"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_jones= pd.read_csv('DowJones.csv')\n",
    "dow_jones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "FyAR5Z3ZFIvk",
    "outputId": "be1713de-4a9f-44b7-d379-7c39f4fc30a1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-f0776650-e799-43d6-b013-96dc41e38f73\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-f0776650-e799-43d6-b013-96dc41e38f73\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Nasdaq.csv to Nasdaq.csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "n70SqXkpFLco",
    "outputId": "5537d893-4722-4c8f-fb65-120b12e136c9"
   },
   "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>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977-09-02</td>\n",
       "      <td>100.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977-09-09</td>\n",
       "      <td>100.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977-09-16</td>\n",
       "      <td>100.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977-09-23</td>\n",
       "      <td>99.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977-09-30</td>\n",
       "      <td>100.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Value\n",
       "0  1977-09-02  100.83\n",
       "1  1977-09-09  100.91\n",
       "2  1977-09-16  100.74\n",
       "3  1977-09-23   99.57\n",
       "4  1977-09-30  100.85"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nasdaq= pd.read_csv('Nasdaq.csv')\n",
    "nasdaq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "t0mSzBcNFPzD",
    "outputId": "f76d5901-bbe8-4ee9-fd37-204523ce793c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-28976010-d4ad-4004-9d69-23e9705a7601\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-28976010-d4ad-4004-9d69-23e9705a7601\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving SP500.csv to SP500 (1).csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "h99Fq7bjFSKE",
    "outputId": "605133e8-c0df-432d-e39f-ea5472d5a4f7"
   },
   "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>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977-09-02</td>\n",
       "      <td>97.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977-09-09</td>\n",
       "      <td>96.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977-09-16</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977-09-23</td>\n",
       "      <td>95.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977-09-30</td>\n",
       "      <td>96.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Value\n",
       "0  1977-09-02  97.45\n",
       "1  1977-09-09  96.37\n",
       "2  1977-09-16  96.48\n",
       "3  1977-09-23  95.04\n",
       "4  1977-09-30  96.53"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp= pd.read_csv('SP500.csv')\n",
    "sp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7goXZAPTFbR3"
   },
   "outputs": [],
   "source": [
    "merge1= pd.merge(dow_jones,nasdaq,on='Date')\n",
    "data=pd.merge(merge1,sp,on='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XZLI26ffFhPy"
   },
   "outputs": [],
   "source": [
    "data.columns=['Date','Dow Jones Value','NASDAQ','SP500']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "KawUrrsgFlMA",
    "outputId": "dee6e0b0-b312-49d1-9fa2-f21121bd7281"
   },
   "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>Date</th>\n",
       "      <th>Dow Jones Value</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1977-09-02</td>\n",
       "      <td>872.31</td>\n",
       "      <td>100.83</td>\n",
       "      <td>97.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977-09-09</td>\n",
       "      <td>857.04</td>\n",
       "      <td>100.91</td>\n",
       "      <td>96.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977-09-16</td>\n",
       "      <td>856.81</td>\n",
       "      <td>100.74</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1977-09-23</td>\n",
       "      <td>839.14</td>\n",
       "      <td>99.57</td>\n",
       "      <td>95.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977-09-30</td>\n",
       "      <td>847.11</td>\n",
       "      <td>100.85</td>\n",
       "      <td>96.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Dow Jones Value  NASDAQ  SP500\n",
       "0  1977-09-02           872.31  100.83  97.45\n",
       "1  1977-09-09           857.04  100.91  96.37\n",
       "2  1977-09-16           856.81  100.74  96.48\n",
       "3  1977-09-23           839.14   99.57  95.04\n",
       "4  1977-09-30           847.11  100.85  96.53"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "colab_type": "code",
    "id": "m-6sYQOIFnf1",
    "outputId": "f01797f3-940a-4262-b41f-3506a6d1ce11"
   },
   "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>Dow Jones Value</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1977-09-02</th>\n",
       "      <td>872.31</td>\n",
       "      <td>100.83</td>\n",
       "      <td>97.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-09</th>\n",
       "      <td>857.04</td>\n",
       "      <td>100.91</td>\n",
       "      <td>96.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-16</th>\n",
       "      <td>856.81</td>\n",
       "      <td>100.74</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-23</th>\n",
       "      <td>839.14</td>\n",
       "      <td>99.57</td>\n",
       "      <td>95.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-30</th>\n",
       "      <td>847.11</td>\n",
       "      <td>100.85</td>\n",
       "      <td>96.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dow Jones Value  NASDAQ  SP500\n",
       "Date                                      \n",
       "1977-09-02           872.31  100.83  97.45\n",
       "1977-09-09           857.04  100.91  96.37\n",
       "1977-09-16           856.81  100.74  96.48\n",
       "1977-09-23           839.14   99.57  95.04\n",
       "1977-09-30           847.11  100.85  96.53"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']=pd.to_datetime(data['Date'])\n",
    "\n",
    "data= data.groupby(['Date']).sum()\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "colab_type": "code",
    "id": "yxeUYRgoFtCg",
    "outputId": "70e8c8a8-db6b-4b55-f987-95cf6e6b9428"
   },
   "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>Dow Jones Value</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1977-09-04</th>\n",
       "      <td>872.31</td>\n",
       "      <td>100.8300</td>\n",
       "      <td>97.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-11</th>\n",
       "      <td>857.04</td>\n",
       "      <td>100.9100</td>\n",
       "      <td>96.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-18</th>\n",
       "      <td>856.81</td>\n",
       "      <td>100.7400</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-25</th>\n",
       "      <td>839.14</td>\n",
       "      <td>99.5700</td>\n",
       "      <td>95.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-10-02</th>\n",
       "      <td>847.11</td>\n",
       "      <td>100.8500</td>\n",
       "      <td>96.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-06</th>\n",
       "      <td>22092.81</td>\n",
       "      <td>6351.5600</td>\n",
       "      <td>2476.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-13</th>\n",
       "      <td>21858.32</td>\n",
       "      <td>6256.5600</td>\n",
       "      <td>2441.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
       "      <td>21674.51</td>\n",
       "      <td>6216.5300</td>\n",
       "      <td>2425.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-27</th>\n",
       "      <td>21813.67</td>\n",
       "      <td>6265.6400</td>\n",
       "      <td>2443.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-03</th>\n",
       "      <td>21865.37</td>\n",
       "      <td>6301.8857</td>\n",
       "      <td>2446.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2088 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dow Jones Value     NASDAQ    SP500\n",
       "Date                                           \n",
       "1977-09-04           872.31   100.8300    97.45\n",
       "1977-09-11           857.04   100.9100    96.37\n",
       "1977-09-18           856.81   100.7400    96.48\n",
       "1977-09-25           839.14    99.5700    95.04\n",
       "1977-10-02           847.11   100.8500    96.53\n",
       "...                     ...        ...      ...\n",
       "2017-08-06         22092.81  6351.5600  2476.83\n",
       "2017-08-13         21858.32  6256.5600  2441.32\n",
       "2017-08-20         21674.51  6216.5300  2425.55\n",
       "2017-08-27         21813.67  6265.6400  2443.05\n",
       "2017-09-03         21865.37  6301.8857  2446.30\n",
       "\n",
       "[2088 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Weekly Sample\n",
    "df=data.resample('W').sum()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VKOVtfRwF2oa"
   },
   "source": [
    "### Visualizing the Time Series Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 724
    },
    "colab_type": "code",
    "id": "spIfXl05FwEC",
    "outputId": "cbca93ac-51bc-41db-e150-9e318d4e6e50"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, dpi=120, figsize=(10,6))\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    data = df[df.columns[i]]\n",
    "    ax.plot(data, color='indianred', linewidth=1)\n",
    "    # Decorations\n",
    "    ax.set_title(df.columns[i])\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vxdTuH1nF9GH"
   },
   "source": [
    "### Check for Stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "id": "rcLryriZF6Gw",
    "outputId": "84c65b21-2ed4-4107-dcaf-1526adba5547"
   },
   "outputs": [
    {
     "name": "stderr",
     "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"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def adfuller_test(series, signif=0.05, name='', verbose=False):\n",
    "    \"\"\"Perform ADFuller to test for Stationarity of given series and print report\"\"\"\n",
    "    r = adfuller(series, autolag='AIC')\n",
    "    output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}\n",
    "    p_value = output['pvalue'] \n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Print Summary\n",
    "    print(f'    Augmented Dickey-Fuller Test on \"{name}\"', \"\\n   \", '-'*47)\n",
    "    print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n",
    "    print(f' Significance Level    = {signif}')\n",
    "    print(f' Test Statistic        = {output[\"test_statistic\"]}')\n",
    "    print(f' No. Lags Chosen       = {output[\"n_lags\"]}')\n",
    "\n",
    "    for key,val in r[4].items():\n",
    "        print(f' Critical value {adjust(key)} = {round(val, 3)}')\n",
    "\n",
    "    if p_value <= signif:\n",
    "        print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n",
    "        print(f\" => Series is Stationary.\")\n",
    "    else:\n",
    "        print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n",
    "        print(f\" => Series is Non-Stationary.\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 708
    },
    "colab_type": "code",
    "id": "jzF2_r55F_jl",
    "outputId": "f4675efe-2eac-4f92-fdca-f04d2aa42da9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"Dow Jones Value\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.3093\n",
      " No. Lags Chosen       = 26\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.9967. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"NASDAQ\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.8567\n",
      " No. Lags Chosen       = 26\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.9925. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"SP500\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.853\n",
      " No. Lags Chosen       = 26\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.9924. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ADF Test on each column\n",
    "for name, column in df.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ue-abJArGFUT"
   },
   "source": [
    "##### All the features are non-stationary\n",
    "\n",
    "### Integrating the Time Series to the order of one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "colab_type": "code",
    "id": "F7KFU-quGCVT",
    "outputId": "052c8f30-42e4-477a-c151-32c6d00cb31d"
   },
   "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>Dow Jones Value</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1977-09-11</th>\n",
       "      <td>-15.27</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>-1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-18</th>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.1700</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-25</th>\n",
       "      <td>-17.67</td>\n",
       "      <td>-1.1700</td>\n",
       "      <td>-1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-10-02</th>\n",
       "      <td>7.97</td>\n",
       "      <td>1.2800</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-10-09</th>\n",
       "      <td>-6.76</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-06</th>\n",
       "      <td>262.50</td>\n",
       "      <td>-23.1200</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-13</th>\n",
       "      <td>-234.49</td>\n",
       "      <td>-95.0000</td>\n",
       "      <td>-35.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-20</th>\n",
       "      <td>-183.81</td>\n",
       "      <td>-40.0300</td>\n",
       "      <td>-15.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-27</th>\n",
       "      <td>139.16</td>\n",
       "      <td>49.1100</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-03</th>\n",
       "      <td>51.70</td>\n",
       "      <td>36.2457</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2087 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dow Jones Value   NASDAQ  SP500\n",
       "Date                                       \n",
       "1977-09-11           -15.27   0.0800  -1.08\n",
       "1977-09-18            -0.23  -0.1700   0.11\n",
       "1977-09-25           -17.67  -1.1700  -1.44\n",
       "1977-10-02             7.97   1.2800   1.49\n",
       "1977-10-09            -6.76   0.7700  -0.56\n",
       "...                     ...      ...    ...\n",
       "2017-08-06           262.50 -23.1200   4.73\n",
       "2017-08-13          -234.49 -95.0000 -35.51\n",
       "2017-08-20          -183.81 -40.0300 -15.77\n",
       "2017-08-27           139.16  49.1100  17.50\n",
       "2017-09-03            51.70  36.2457   3.25\n",
       "\n",
       "[2087 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1st difference\n",
    "df_differenced = df.diff().dropna()\n",
    "df_differenced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 708
    },
    "colab_type": "code",
    "id": "He8874v5GK20",
    "outputId": "2d3dc347-717e-43df-f22b-c0a5a77bb020"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"Dow Jones Value\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -9.1421\n",
      " No. Lags Chosen       = 26\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"NASDAQ\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -8.8072\n",
      " No. Lags Chosen       = 25\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"SP500\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -8.536\n",
      " No. Lags Chosen       = 26\n",
      " Critical value 1%     = -3.434\n",
      " Critical value 5%     = -2.863\n",
      " Critical value 10%    = -2.568\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ADF Test on each column of 1st Differences Dataframe\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "P1LduzuPGN8H",
    "outputId": "58cc0fda-74ab-4a9a-9492-bfa752abfc8c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras import backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 199
    },
    "colab_type": "code",
    "id": "I2MTzvlWGkpE",
    "outputId": "300ea4fb-58f3-4335-f4b3-d1b73c7a51ee"
   },
   "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>Dow Jones Value</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495937</td>\n",
       "      <td>0.511855</td>\n",
       "      <td>0.509634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.496626</td>\n",
       "      <td>0.511810</td>\n",
       "      <td>0.510089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.495827</td>\n",
       "      <td>0.511632</td>\n",
       "      <td>0.509497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.497001</td>\n",
       "      <td>0.512069</td>\n",
       "      <td>0.510616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.496327</td>\n",
       "      <td>0.511978</td>\n",
       "      <td>0.509833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dow Jones Value    NASDAQ     SP500\n",
       "0         0.495937  0.511855  0.509634\n",
       "1         0.496626  0.511810  0.510089\n",
       "2         0.495827  0.511632  0.509497\n",
       "3         0.497001  0.512069  0.510616\n",
       "4         0.496327  0.511978  0.509833"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mmsc=MinMaxScaler()\n",
    "df_differenced= mmsc.fit_transform(df_differenced)\n",
    "df_differenced=pd.DataFrame(df_differenced)\n",
    "df_differenced.columns=df.columns\n",
    "df_differenced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "GjvLbUdMJLqB",
    "outputId": "d311f654-5df1-445a-9363-1ea0abd4da55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2087, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_differenced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "hkaw8_Ylu9FQ",
    "outputId": "73345e76-6210-40bc-897b-c488c9a9dcff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 1, 2) (1460,) (627, 1, 2) (627,)\n"
     ]
    }
   ],
   "source": [
    "# split into train and test sets\n",
    "values = df_differenced.values\n",
    "n_train_hours = 1460\n",
    "train = values[:n_train_hours, :]\n",
    "test = values[n_train_hours:, :]\n",
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :-1], train[:, -1]\n",
    "test_X, test_y = test[:, :-1], test[:, -1]\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
    "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Yp1smFphvAA9",
    "outputId": "f9e1fcb5-ead5-46d3-d16e-2910deefdf6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1460 samples, validate on 627 samples\n",
      "Epoch 1/30\n",
      "1460/1460 [==============================] - 0s 321us/step - loss: 0.1179 - rmse: 0.3255 - val_loss: 0.0174 - val_rmse: 0.1305\n",
      "Epoch 2/30\n",
      "1460/1460 [==============================] - 0s 88us/step - loss: 0.0027 - rmse: 0.0356 - val_loss: 4.9974e-04 - val_rmse: 0.0129\n",
      "Epoch 3/30\n",
      "1460/1460 [==============================] - 0s 80us/step - loss: 2.6709e-04 - rmse: 0.0050 - val_loss: 3.9919e-04 - val_rmse: 0.0060\n",
      "Epoch 4/30\n",
      "1460/1460 [==============================] - 0s 82us/step - loss: 2.4770e-04 - rmse: 0.0032 - val_loss: 3.9484e-04 - val_rmse: 0.0060\n",
      "Epoch 5/30\n",
      "1460/1460 [==============================] - 0s 80us/step - loss: 2.4547e-04 - rmse: 0.0032 - val_loss: 3.9029e-04 - val_rmse: 0.0059\n",
      "Epoch 6/30\n",
      "1460/1460 [==============================] - 0s 92us/step - loss: 2.4306e-04 - rmse: 0.0032 - val_loss: 3.8530e-04 - val_rmse: 0.0059\n",
      "Epoch 7/30\n",
      "1460/1460 [==============================] - 0s 81us/step - loss: 2.4035e-04 - rmse: 0.0032 - val_loss: 3.7991e-04 - val_rmse: 0.0059\n",
      "Epoch 8/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 2.3741e-04 - rmse: 0.0032 - val_loss: 3.7415e-04 - val_rmse: 0.0059\n",
      "Epoch 9/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 2.3426e-04 - rmse: 0.0032 - val_loss: 3.6804e-04 - val_rmse: 0.0059\n",
      "Epoch 10/30\n",
      "1460/1460 [==============================] - 0s 78us/step - loss: 2.3090e-04 - rmse: 0.0032 - val_loss: 3.6158e-04 - val_rmse: 0.0059\n",
      "Epoch 11/30\n",
      "1460/1460 [==============================] - 0s 81us/step - loss: 2.2736e-04 - rmse: 0.0032 - val_loss: 3.5480e-04 - val_rmse: 0.0059\n",
      "Epoch 12/30\n",
      "1460/1460 [==============================] - 0s 78us/step - loss: 2.2364e-04 - rmse: 0.0032 - val_loss: 3.4768e-04 - val_rmse: 0.0058\n",
      "Epoch 13/30\n",
      "1460/1460 [==============================] - 0s 84us/step - loss: 2.1977e-04 - rmse: 0.0032 - val_loss: 3.4027e-04 - val_rmse: 0.0058\n",
      "Epoch 14/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 2.1575e-04 - rmse: 0.0032 - val_loss: 3.3256e-04 - val_rmse: 0.0057\n",
      "Epoch 15/30\n",
      "1460/1460 [==============================] - 0s 78us/step - loss: 2.1160e-04 - rmse: 0.0031 - val_loss: 3.2460e-04 - val_rmse: 0.0057\n",
      "Epoch 16/30\n",
      "1460/1460 [==============================] - 0s 81us/step - loss: 2.0734e-04 - rmse: 0.0031 - val_loss: 3.1640e-04 - val_rmse: 0.0056\n",
      "Epoch 17/30\n",
      "1460/1460 [==============================] - 0s 77us/step - loss: 2.0296e-04 - rmse: 0.0031 - val_loss: 3.0799e-04 - val_rmse: 0.0056\n",
      "Epoch 18/30\n",
      "1460/1460 [==============================] - 0s 85us/step - loss: 1.9849e-04 - rmse: 0.0030 - val_loss: 2.9941e-04 - val_rmse: 0.0055\n",
      "Epoch 19/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 1.9393e-04 - rmse: 0.0030 - val_loss: 2.9066e-04 - val_rmse: 0.0054\n",
      "Epoch 20/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 1.8929e-04 - rmse: 0.0030 - val_loss: 2.8179e-04 - val_rmse: 0.0053\n",
      "Epoch 21/30\n",
      "1460/1460 [==============================] - 0s 82us/step - loss: 1.8458e-04 - rmse: 0.0029 - val_loss: 2.7280e-04 - val_rmse: 0.0053\n",
      "Epoch 22/30\n",
      "1460/1460 [==============================] - 0s 78us/step - loss: 1.7980e-04 - rmse: 0.0029 - val_loss: 2.6373e-04 - val_rmse: 0.0052\n",
      "Epoch 23/30\n",
      "1460/1460 [==============================] - 0s 86us/step - loss: 1.7498e-04 - rmse: 0.0029 - val_loss: 2.5459e-04 - val_rmse: 0.0051\n",
      "Epoch 24/30\n",
      "1460/1460 [==============================] - 0s 85us/step - loss: 1.7010e-04 - rmse: 0.0028 - val_loss: 2.4540e-04 - val_rmse: 0.0050\n",
      "Epoch 25/30\n",
      "1460/1460 [==============================] - 0s 84us/step - loss: 1.6519e-04 - rmse: 0.0028 - val_loss: 2.3618e-04 - val_rmse: 0.0049\n",
      "Epoch 26/30\n",
      "1460/1460 [==============================] - 0s 79us/step - loss: 1.6025e-04 - rmse: 0.0028 - val_loss: 2.2695e-04 - val_rmse: 0.0049\n",
      "Epoch 27/30\n",
      "1460/1460 [==============================] - 0s 80us/step - loss: 1.5529e-04 - rmse: 0.0027 - val_loss: 2.1773e-04 - val_rmse: 0.0048\n",
      "Epoch 28/30\n",
      "1460/1460 [==============================] - 0s 81us/step - loss: 1.5032e-04 - rmse: 0.0027 - val_loss: 2.0854e-04 - val_rmse: 0.0047\n",
      "Epoch 29/30\n",
      "1460/1460 [==============================] - 0s 78us/step - loss: 1.4534e-04 - rmse: 0.0027 - val_loss: 1.9940e-04 - val_rmse: 0.0046\n",
      "Epoch 30/30\n",
      "1460/1460 [==============================] - 0s 82us/step - loss: 1.4037e-04 - rmse: 0.0026 - val_loss: 1.9032e-04 - val_rmse: 0.0045\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "\treturn backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))\n",
    "\n",
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(70, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=[rmse])\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=30, batch_size=30, validation_data=(test_X, test_y), verbose=1, shuffle=False)\n",
    "# plot history\n",
    "pyplot.plot(history.history['rmse'], label='train')\n",
    "pyplot.plot(history.history['val_rmse'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  }
 ],
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