RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d047f7ba",
   "metadata": {},
   "source": [
    "### Setting up data for RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52660b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6aaf50e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_csv(\"Dataset/international-airline-passengers.csv\" ,usecols=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4532c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <th>...</th>\n",
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       "      <td>606</td>\n",
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       "      <td>508</td>\n",
       "    </tr>\n",
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       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60\n",
       "0                                                  112                             \n",
       "1                                                  118                             \n",
       "2                                                  132                             \n",
       "3                                                  129                             \n",
       "4                                                  121                             \n",
       "..                                                 ...                             \n",
       "139                                                606                             \n",
       "140                                                508                             \n",
       "141                                                461                             \n",
       "142                                                390                             \n",
       "143                                                432                             \n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b0cfe86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>280.298611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>119.966317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>265.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>360.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>622.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60\n",
       "count                                         144.000000                             \n",
       "mean                                          280.298611                             \n",
       "std                                           119.966317                             \n",
       "min                                           104.000000                             \n",
       "25%                                           180.000000                             \n",
       "50%                                           265.500000                             \n",
       "75%                                           360.500000                             \n",
       "max                                           622.000000                             "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "749ae21c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = df.values.astype('float32')\n",
    "values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29565763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 48)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(values.shape[0]*0.67)\n",
    "test_size = values.shape[0] - train_size\n",
    "train_size , test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61e5bb90",
   "metadata": {},
   "outputs": [
    {
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       "       [306.]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = values[0:train_size]\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d26d3af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[315.],\n",
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       "       [432.]], dtype=float32)"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = values[train_size:]\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b4c5bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "train = scaler.fit_transform(train)\n",
    "test = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f004e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data,k):\n",
    "    dataX, dataY = [],[]\n",
    "    for i in range(data.shape[0]- k):\n",
    "        x = data[i:i+k,0]\n",
    "        y = data[i+k,0]\n",
    "        dataX.append(x)\n",
    "        dataY.append(y)\n",
    "    return np.array(dataX),np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "708350f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 12\n",
    "trainX,trainY = create_dataset(train,look_back)\n",
    "testX,testY = create_dataset(test,look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33801b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 12, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX = np.reshape(trainX,(trainX.shape[0],trainX.shape[1],1))\n",
    "testX = np.reshape(testX,(testX.shape[0],testX.shape[1],1))\n",
    "trainX.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f178094d",
   "metadata": {},
   "source": [
    "### Setting up RNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "99992c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import SimpleRNN, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2ce03b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "84/84 [==============================] - 1s 3ms/step - loss: 0.4201\n",
      "Epoch 2/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.1754\n",
      "Epoch 3/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.1090\n",
      "Epoch 4/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0837\n",
      "Epoch 5/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0667\n",
      "Epoch 6/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0550\n",
      "Epoch 7/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0464\n",
      "Epoch 8/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0394\n",
      "Epoch 9/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0345\n",
      "Epoch 10/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0305\n",
      "Epoch 11/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0270\n",
      "Epoch 12/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0239\n",
      "Epoch 13/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0217\n",
      "Epoch 14/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0201\n",
      "Epoch 15/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0183\n",
      "Epoch 16/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0173\n",
      "Epoch 17/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0156\n",
      "Epoch 18/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0153\n",
      "Epoch 19/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0142\n",
      "Epoch 20/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0132\n",
      "Epoch 21/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0128\n",
      "Epoch 22/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0119\n",
      "Epoch 23/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0113\n",
      "Epoch 24/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0109\n",
      "Epoch 25/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0103\n",
      "Epoch 26/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0102\n",
      "Epoch 27/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0096\n",
      "Epoch 28/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0096\n",
      "Epoch 29/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0091\n",
      "Epoch 30/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0092\n",
      "Epoch 31/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0087\n",
      "Epoch 32/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0083\n",
      "Epoch 33/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0082\n",
      "Epoch 34/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0082\n",
      "Epoch 35/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0078\n",
      "Epoch 36/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0077\n",
      "Epoch 37/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0075\n",
      "Epoch 38/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0073\n",
      "Epoch 39/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0074\n",
      "Epoch 40/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0070\n",
      "Epoch 41/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 42/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0069\n",
      "Epoch 43/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0064\n",
      "Epoch 44/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0063\n",
      "Epoch 45/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0063\n",
      "Epoch 46/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0058\n",
      "Epoch 47/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0054\n",
      "Epoch 48/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0062\n",
      "Epoch 49/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0062\n",
      "Epoch 50/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0055\n",
      "Epoch 51/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0054\n",
      "Epoch 52/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0055\n",
      "Epoch 53/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0050\n",
      "Epoch 54/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0050\n",
      "Epoch 55/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0052\n",
      "Epoch 56/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0049\n",
      "Epoch 57/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0049\n",
      "Epoch 58/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0051\n",
      "Epoch 59/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0048\n",
      "Epoch 60/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0044\n",
      "Epoch 61/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0042\n",
      "Epoch 62/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0045\n",
      "Epoch 63/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0043\n",
      "Epoch 64/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0043\n",
      "Epoch 65/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0040\n",
      "Epoch 66/100\n",
      "84/84 [==============================] - 0s 4ms/step - loss: 0.0043\n",
      "Epoch 67/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0041\n",
      "Epoch 68/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0044\n",
      "Epoch 69/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0039\n",
      "Epoch 70/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0039\n",
      "Epoch 71/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0038\n",
      "Epoch 72/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0039\n",
      "Epoch 73/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0037\n",
      "Epoch 74/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0038\n",
      "Epoch 75/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0037\n",
      "Epoch 76/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0039\n",
      "Epoch 77/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0036\n",
      "Epoch 78/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0037\n",
      "Epoch 79/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0036\n",
      "Epoch 80/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 81/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 82/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 83/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0035\n",
      "Epoch 84/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0035\n",
      "Epoch 85/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 86/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 87/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0038\n",
      "Epoch 88/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0031\n",
      "Epoch 89/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 90/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0034\n",
      "Epoch 91/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0032\n",
      "Epoch 92/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0032\n",
      "Epoch 93/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0033\n",
      "Epoch 94/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0031\n",
      "Epoch 95/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0035\n",
      "Epoch 96/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0031\n",
      "Epoch 97/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0030\n",
      "Epoch 98/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0033\n",
      "Epoch 99/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0036\n",
      "Epoch 100/100\n",
      "84/84 [==============================] - 0s 3ms/step - loss: 0.0030\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20198364400>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(SimpleRNN(4,input_shape=(look_back,1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss = 'mean_squared_error',optimizer='adam')\n",
    "model.fit(trainX,trainY,epochs=100,batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486be7c0",
   "metadata": {},
   "source": [
    "### Analysing output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51f47680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step\n",
      "3/3 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((36,), (36,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict = model.predict(testX)\n",
    "testPredict = scaler.inverse_transform(testPredict).ravel()\n",
    "\n",
    "trainPredict = model.predict(trainX)\n",
    "trainPredict = scaler.inverse_transform(trainPredict).ravel()\n",
    "\n",
    "testTrue = scaler.inverse_transform([testY]).ravel()\n",
    "trainTrue = scaler.inverse_transform([trainY]).ravel()\n",
    "\n",
    "testPredict.shape,testTrue.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c812ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "412a6db1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trainTrue, c='g')\n",
    "plt.plot(trainPredict, c='b');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c297857b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combinedPredicted = np.concatenate((trainPredict, testPredict))\n",
    "combinedTrue = np.concatenate((trainTrue, testTrue))\n",
    "plt.plot(combinedTrue, c = 'g')\n",
    "plt.plot(combinedPredicted, c = 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1675773",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}