Time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xpAuMVCwfWs8"
   },
   "source": [
    "# Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nxJfRe4bfYVA"
   },
   "source": [
    "## Part 1 - Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ir9zwETrfbrp"
   },
   "source": [
    "### Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1SF1ionRte9T"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nQ47JAxrgmaL"
   },
   "source": [
    "### Importing the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "khMVRyk4uuNE"
   },
   "outputs": [],
   "source": [
    "dataset_train = pd.read_csv(\"Google_Stock_Price_Train.csv\")\n",
    "training_set = dataset_train.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HT8_2UJegtG5"
   },
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D31qxH7mwsLh"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "SC = MinMaxScaler(feature_range=(0,1))\n",
    "training_set_scaled = SC.fit_transform(training_set)\n",
    "#creating data structure with 60 timestep and 1 output\n",
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(60,1258):\n",
    "  x_train.append(training_set_scaled[i-60:i,0])\n",
    "  y_train.append(training_set_scaled[i,0])\n",
    "x_train, y_train = np.array(x_train),np.array(y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JyYgYocqhNUg"
   },
   "source": [
    "### Creating a data structure with 60 timesteps and 1 output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8yaN7Zvi95l"
   },
   "source": [
    "### Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UijJ9PWNzsuE"
   },
   "outputs": [],
   "source": [
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZRRSOJeVjEWV"
   },
   "source": [
    "## Part 2 - Building and Training the RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k4XV88JMjHXG"
   },
   "source": [
    "### Importing the Keras libraries and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7TypzLFy2N9D"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FEIE-1s9jNzC"
   },
   "source": [
    "### Initialising the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1vq5t38d2k5P"
   },
   "outputs": [],
   "source": [
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "62eg1OPGjT8z"
   },
   "source": [
    "### Adding the first LSTM layer and some Dropout regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KrFvi0Tr2puI"
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3XBIYLyOjlMx"
   },
   "source": [
    "### Adding a second LSTM layer and some Dropout regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8182khwA8ziE"
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ey3fHVnGj1cu"
   },
   "source": [
    "### Adding a third LSTM layer and some Dropout regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RswQckLn85Hp"
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SYTrtfTmj933"
   },
   "source": [
    "### Adding a fourth LSTM layer and some Dropout regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ytL9XSCz9COV"
   },
   "outputs": [],
   "source": [
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ABI6rOIkHhk"
   },
   "source": [
    "### Adding the output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P6ReHaCE9ddc"
   },
   "outputs": [],
   "source": [
    "regressor.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zLx4K7uUkPSh"
   },
   "source": [
    "### Compiling the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RmlFV-mpBTF3"
   },
   "outputs": [],
   "source": [
    "regressor.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-mPhwKGkkebi"
   },
   "source": [
    "### Fitting the RNN to the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Tf5DSruC5h5",
    "outputId": "4bba0d6c-c78b-40c2-ecdf-fdaa4b8e0e76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "38/38 [==============================] - 24s 105ms/step - loss: 0.0993\n",
      "Epoch 2/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0066\n",
      "Epoch 3/100\n",
      "38/38 [==============================] - 4s 106ms/step - loss: 0.0053\n",
      "Epoch 4/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0046\n",
      "Epoch 5/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0049\n",
      "Epoch 6/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0041\n",
      "Epoch 7/100\n",
      "38/38 [==============================] - 4s 106ms/step - loss: 0.0054\n",
      "Epoch 8/100\n",
      "38/38 [==============================] - 4s 107ms/step - loss: 0.0043\n",
      "Epoch 9/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0041\n",
      "Epoch 10/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0049\n",
      "Epoch 11/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0047\n",
      "Epoch 12/100\n",
      "38/38 [==============================] - 4s 106ms/step - loss: 0.0041\n",
      "Epoch 13/100\n",
      "38/38 [==============================] - 4s 105ms/step - loss: 0.0042\n",
      "Epoch 14/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0031\n",
      "Epoch 15/100\n",
      "38/38 [==============================] - 4s 107ms/step - loss: 0.0033\n",
      "Epoch 16/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0037\n",
      "Epoch 17/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0036\n",
      "Epoch 18/100\n",
      "38/38 [==============================] - 4s 107ms/step - loss: 0.0039\n",
      "Epoch 19/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0043\n",
      "Epoch 20/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0034\n",
      "Epoch 21/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0037\n",
      "Epoch 22/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0036\n",
      "Epoch 23/100\n",
      "38/38 [==============================] - 4s 118ms/step - loss: 0.0042\n",
      "Epoch 24/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0030\n",
      "Epoch 25/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0030\n",
      "Epoch 26/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0032\n",
      "Epoch 27/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0032\n",
      "Epoch 28/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0035\n",
      "Epoch 29/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0032\n",
      "Epoch 30/100\n",
      "38/38 [==============================] - 4s 116ms/step - loss: 0.0028\n",
      "Epoch 31/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0033\n",
      "Epoch 32/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0029\n",
      "Epoch 33/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0027\n",
      "Epoch 34/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0034\n",
      "Epoch 35/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0027\n",
      "Epoch 36/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0027\n",
      "Epoch 37/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0028\n",
      "Epoch 38/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0028\n",
      "Epoch 39/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0025\n",
      "Epoch 40/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0024\n",
      "Epoch 41/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0025\n",
      "Epoch 42/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0024\n",
      "Epoch 43/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0028\n",
      "Epoch 44/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0024\n",
      "Epoch 45/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0027\n",
      "Epoch 46/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0023\n",
      "Epoch 47/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0024\n",
      "Epoch 48/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0023\n",
      "Epoch 49/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0024\n",
      "Epoch 50/100\n",
      "38/38 [==============================] - 4s 117ms/step - loss: 0.0022\n",
      "Epoch 51/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0025\n",
      "Epoch 52/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0022\n",
      "Epoch 53/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0026\n",
      "Epoch 54/100\n",
      "38/38 [==============================] - 4s 116ms/step - loss: 0.0020\n",
      "Epoch 55/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0023\n",
      "Epoch 56/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0021\n",
      "Epoch 57/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0021\n",
      "Epoch 58/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0021\n",
      "Epoch 59/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0023\n",
      "Epoch 60/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0019\n",
      "Epoch 61/100\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0019\n",
      "Epoch 62/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0018\n",
      "Epoch 63/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0019\n",
      "Epoch 64/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0020\n",
      "Epoch 65/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0024\n",
      "Epoch 66/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0021\n",
      "Epoch 67/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0021\n",
      "Epoch 68/100\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0017\n",
      "Epoch 69/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0021\n",
      "Epoch 70/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0020\n",
      "Epoch 71/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0021\n",
      "Epoch 72/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0020\n",
      "Epoch 73/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0021\n",
      "Epoch 74/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0019\n",
      "Epoch 75/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0017\n",
      "Epoch 76/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0019\n",
      "Epoch 77/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0017\n",
      "Epoch 78/100\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0019\n",
      "Epoch 79/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0017\n",
      "Epoch 80/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0020\n",
      "Epoch 81/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0016\n",
      "Epoch 82/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0017\n",
      "Epoch 83/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0015\n",
      "Epoch 84/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0015\n",
      "Epoch 85/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0016\n",
      "Epoch 86/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0015\n",
      "Epoch 87/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0016\n",
      "Epoch 88/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0018\n",
      "Epoch 89/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0017\n",
      "Epoch 90/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0015\n",
      "Epoch 91/100\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0015\n",
      "Epoch 92/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0015\n",
      "Epoch 93/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0014\n",
      "Epoch 94/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0016\n",
      "Epoch 95/100\n",
      "38/38 [==============================] - 4s 116ms/step - loss: 0.0017\n",
      "Epoch 96/100\n",
      "38/38 [==============================] - 4s 108ms/step - loss: 0.0014\n",
      "Epoch 97/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0015\n",
      "Epoch 98/100\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0015\n",
      "Epoch 99/100\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0014\n",
      "Epoch 100/100\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0015\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb96ca5dd10>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_train, y_train, epochs=100, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4hRau_lIkrE8"
   },
   "source": [
    "## Part 3 - Making the predictions and visualising the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SgJO6qEDksxD"
   },
   "source": [
    "### Getting the real stock price of 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zy49_B1TOoVm"
   },
   "outputs": [],
   "source": [
    "dataset_test = pd.read_csv(\"Google_Stock_Price_Test.csv\")\n",
    "real_stock_price = dataset_test.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xtV5KC8ri6Qp",
    "outputId": "eff5fa10-142b-407e-b114-96100b3b9eeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[778.81]\n",
      " [788.36]\n",
      " [786.08]\n",
      " [795.26]\n",
      " [806.4 ]\n",
      " [807.86]\n",
      " [805.  ]\n",
      " [807.14]\n",
      " [807.48]\n",
      " [807.08]\n",
      " [805.81]\n",
      " [805.12]\n",
      " [806.91]\n",
      " [807.25]\n",
      " [822.3 ]\n",
      " [829.62]\n",
      " [837.81]\n",
      " [834.71]\n",
      " [814.66]\n",
      " [796.86]]\n"
     ]
    }
   ],
   "source": [
    "print(real_stock_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GrvrLblxkz42"
   },
   "source": [
    "### Getting the predicted stock price of 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BIBMsMORqZrV"
   },
   "outputs": [],
   "source": [
    "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis=0)\n",
    "inputs = dataset_total[len(dataset_total)-len(dataset_test)-60:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = SC.transform(inputs)\n",
    "x_test=[]\n",
    "for i in range(60,80):\n",
    "  x_test.append(inputs[i-60:i,0])\n",
    "x_test = np.array(x_test)\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "predicted_stock_price = regressor.predict(x_test)\n",
    "predicted_stock_price = SC.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hUx7o5LG7cUP",
    "outputId": "dc35f493-a541-4608-d7bb-a1e05c119f89"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[780.8591 ],\n",
       "       [778.3826 ],\n",
       "       [778.3648 ],\n",
       "       [779.46216],\n",
       "       [782.48883],\n",
       "       [788.0436 ],\n",
       "       [793.49884],\n",
       "       [796.2597 ],\n",
       "       [797.09155],\n",
       "       [796.9657 ],\n",
       "       [796.5006 ],\n",
       "       [795.8971 ],\n",
       "       [795.4063 ],\n",
       "       [795.67566],\n",
       "       [796.5242 ],\n",
       "       [800.7801 ],\n",
       "       [807.44354],\n",
       "       [814.954  ],\n",
       "       [819.6862 ],\n",
       "       [816.9458 ]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_stock_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFTNs3YHk6FQ"
   },
   "source": [
    "### Visualising the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "6m1sCUyGAgNB",
    "outputId": "d566aec2-f0cd-4eab-ab13-cbfef14e6e70"
   },
   "outputs": [
    {
     "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": [
    "plt.plot(real_stock_price, color='red', label='real_google_stock_price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='google_stock_price_predicted')\n",
    "plt.title('Google_stock_price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('google_stock_price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Time_series.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}