GRU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://medium.com/aimonks/multivariate-timeseries-analysis-using-tensorflow-9554e607077a#:~:text=In%20summary%2C%20multivariate%20time%20series,variables%20to%20improve%20prediction%20accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "import yfinance as yf\n",
    "#from datetie import datetime\n",
    "\n",
    "#Read the csv file\n",
    "# df = pd.read_csv('data/GE.csv')\n",
    "#df = yf.download('ADANIPOWER.NS', start = \"2016-01-01\", end = \"2023-12-30\")\n",
    "\n",
    "#print(df.head()) #7 columns, including the Date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>34.400002</td>\n",
       "      <td>32.299999</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>12331921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.700001</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.700001</td>\n",
       "      <td>32.700001</td>\n",
       "      <td>15329003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>32.849998</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>32.849998</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>12091589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>34.549999</td>\n",
       "      <td>35.150002</td>\n",
       "      <td>33.599998</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>12298429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>33.799999</td>\n",
       "      <td>33.799999</td>\n",
       "      <td>31.299999</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>10495393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close    Volume\n",
       "0  2016-01-01  32.500000  34.400002  32.299999  34.000000  34.000000  12331921\n",
       "1  2016-01-04  34.000000  34.700001  32.000000  32.700001  32.700001  15329003\n",
       "2  2016-01-05  32.849998  34.599998  32.849998  34.299999  34.299999  12091589\n",
       "3  2016-01-06  34.549999  35.150002  33.599998  34.500000  34.500000  12298429\n",
       "4  2016-01-07  33.799999  33.799999  31.299999  32.000000  32.000000  10495393"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Adani_Power.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26171e69b50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df.Date, df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\bnaga\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Dense, LSTM, GRU #can train using either GRU or LSTM\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Open', 'High', 'Low', 'Close', 'Adj Close']\n"
     ]
    }
   ],
   "source": [
    "# convert the Date column to DateTime from Object type\n",
    "train_set_dates = pd.to_datetime(df['Date'])\n",
    "train_set_dates.head()\n",
    "\n",
    "# extract the required features (in this we are omiting the volume from feature list)\n",
    "columns = list(df.columns[1:6])\n",
    "print(columns)\n",
    "\n",
    "df_train = df[columns].astype(float)\n",
    "df_train.head()\n",
    "\n",
    "# Normalize the data using the Standard scaler\n",
    "standard_scaler = StandardScaler()\n",
    "scalar = standard_scaler.fit(df_train)\n",
    "scaled_training_df = scalar.transform(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape: (1961, 14, 5)\n",
      "trainY shape: (1961, 1)\n"
     ]
    }
   ],
   "source": [
    "trainX = []\n",
    "trainY = []\n",
    "\n",
    "future_no_of_days = 1 # num of days to predict the future values\n",
    "past_no_of_days = 14 # number of days on which the prediction is computed\n",
    "\n",
    "for i in range(past_no_of_days, len(scaled_training_df) - future_no_of_days+1):\n",
    "    trainX.append(scaled_training_df[i - past_no_of_days:i, 0:df_train.shape[1]])\n",
    "    trainY.append(scaled_training_df[i + future_no_of_days - 1:i + future_no_of_days, 0])\n",
    "    \n",
    "trainX, trainY = np.array(trainX), np.array(trainY)\n",
    "\n",
    "print('trainX shape: {}'.format(trainX.shape))\n",
    "print('trainY shape: {}'.format(trainY.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\bnaga\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\bnaga\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru (GRU)                   (None, 14, 64)            13632     \n",
      "                                                                 \n",
      " gru_1 (GRU)                 (None, 32)                9408      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23073 (90.13 KB)\n",
      "Trainable params: 23073 (90.13 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(GRU(64, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))\n",
    "model.add(GRU(32, activation='relu', return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(trainY.shape[1]))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "WARNING:tensorflow:From C:\\Users\\bnaga\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "111/111 [==============================] - 9s 29ms/step - loss: 0.1104 - val_loss: 0.0531\n",
      "Epoch 2/50\n",
      "111/111 [==============================] - 2s 18ms/step - loss: 0.0365 - val_loss: 0.0227\n",
      "Epoch 3/50\n",
      "111/111 [==============================] - 2s 16ms/step - loss: 0.0289 - val_loss: 0.0386\n",
      "Epoch 4/50\n",
      "111/111 [==============================] - 2s 20ms/step - loss: 0.0225 - val_loss: 0.0319\n",
      "Epoch 5/50\n",
      "111/111 [==============================] - 2s 21ms/step - loss: 0.0269 - val_loss: 0.0115\n",
      "Epoch 6/50\n",
      "111/111 [==============================] - 2s 17ms/step - loss: 0.0282 - val_loss: 0.0142\n",
      "Epoch 7/50\n",
      "111/111 [==============================] - 2s 16ms/step - loss: 0.0278 - val_loss: 0.0143\n",
      "Epoch 8/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0277 - val_loss: 0.0269\n",
      "Epoch 9/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0218 - val_loss: 0.0411\n",
      "Epoch 10/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0227 - val_loss: 0.0426\n",
      "Epoch 11/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0251 - val_loss: 0.0208\n",
      "Epoch 12/50\n",
      "111/111 [==============================] - 1s 12ms/step - loss: 0.0237 - val_loss: 0.0100\n",
      "Epoch 13/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0228 - val_loss: 0.0497\n",
      "Epoch 14/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0250 - val_loss: 0.0445\n",
      "Epoch 15/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0240 - val_loss: 0.0735\n",
      "Epoch 16/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0223 - val_loss: 0.0152\n",
      "Epoch 17/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0195 - val_loss: 0.0436\n",
      "Epoch 18/50\n",
      "111/111 [==============================] - 2s 13ms/step - loss: 0.0216 - val_loss: 0.0065\n",
      "Epoch 19/50\n",
      "111/111 [==============================] - 2s 17ms/step - loss: 0.0195 - val_loss: 0.0128\n",
      "Epoch 20/50\n",
      "111/111 [==============================] - 2s 17ms/step - loss: 0.0188 - val_loss: 0.0467\n",
      "Epoch 21/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0238 - val_loss: 0.0063\n",
      "Epoch 22/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0212 - val_loss: 0.0227\n",
      "Epoch 23/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0185 - val_loss: 0.0315\n",
      "Epoch 24/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0190 - val_loss: 0.0474\n",
      "Epoch 25/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0220 - val_loss: 0.1416\n",
      "Epoch 26/50\n",
      "111/111 [==============================] - 2s 20ms/step - loss: 0.0240 - val_loss: 0.0203\n",
      "Epoch 27/50\n",
      "111/111 [==============================] - 2s 21ms/step - loss: 0.0197 - val_loss: 0.0055\n",
      "Epoch 28/50\n",
      "111/111 [==============================] - 2s 18ms/step - loss: 0.0290 - val_loss: 0.0105\n",
      "Epoch 29/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0209 - val_loss: 0.0478\n",
      "Epoch 30/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0205 - val_loss: 0.0050\n",
      "Epoch 31/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0215 - val_loss: 0.0506\n",
      "Epoch 32/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0235 - val_loss: 0.0076\n",
      "Epoch 33/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0212 - val_loss: 0.0071\n",
      "Epoch 34/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0190 - val_loss: 0.0253\n",
      "Epoch 35/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0210 - val_loss: 0.0089\n",
      "Epoch 36/50\n",
      "111/111 [==============================] - 2s 16ms/step - loss: 0.0251 - val_loss: 0.0364\n",
      "Epoch 37/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0195 - val_loss: 0.0631\n",
      "Epoch 38/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0195 - val_loss: 0.0511\n",
      "Epoch 39/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0191 - val_loss: 0.0093\n",
      "Epoch 40/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0236 - val_loss: 0.0181\n",
      "Epoch 41/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0205 - val_loss: 0.0219\n",
      "Epoch 42/50\n",
      "111/111 [==============================] - 2s 16ms/step - loss: 0.0245 - val_loss: 0.0084\n",
      "Epoch 43/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0244 - val_loss: 0.0092\n",
      "Epoch 44/50\n",
      "111/111 [==============================] - 2s 17ms/step - loss: 0.0203 - val_loss: 0.0046\n",
      "Epoch 45/50\n",
      "111/111 [==============================] - 2s 15ms/step - loss: 0.0208 - val_loss: 0.0088\n",
      "Epoch 46/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0232 - val_loss: 0.0410\n",
      "Epoch 47/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0199 - val_loss: 0.0048\n",
      "Epoch 48/50\n",
      "111/111 [==============================] - 2s 16ms/step - loss: 0.0220 - val_loss: 0.0077\n",
      "Epoch 49/50\n",
      "111/111 [==============================] - 2s 14ms/step - loss: 0.0164 - val_loss: 0.0642\n",
      "Epoch 50/50\n",
      "111/111 [==============================] - 1s 13ms/step - loss: 0.0257 - val_loss: 0.0192\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2617f47ff90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit the model\n",
    "history = model.fit(trainX, trainY, epochs=50, batch_size=16, validation_split=0.1, verbose=1)\n",
    "\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bnaga\\AppData\\Local\\Temp\\ipykernel_18480\\432596278.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  original['Date']=pd.to_datetime(original['Date'])\n"
     ]
    }
   ],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "from pandas.tseries.offsets import CustomBusinessDay\n",
    "us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())\n",
    "\n",
    "n_past = 90\n",
    "n_days_for_prediction=90  #let us predict past 15 days\n",
    "\n",
    "predict_period_dates = pd.date_range(list(train_set_dates)[-n_past], periods=n_days_for_prediction, freq=us_bd).tolist() # instead of us_bd we can give '1d' etc.\n",
    "\n",
    "#Make prediction\n",
    "prediction = model.predict(trainX[-n_days_for_prediction:]) #shape = (n, 1) where n is the n_days_for_prediction\n",
    "\n",
    "# predict the future price\n",
    "prediction_copies = np.repeat(prediction, df_train.shape[1], axis=-1)\n",
    "y_pred_future = scalar.inverse_transform(prediction_copies)[:,0]\n",
    "\n",
    "# Convert timestamp to date\n",
    "forecast_dates = []\n",
    "for time_i in predict_period_dates:\n",
    "    forecast_dates.append(time_i.date())\n",
    "\n",
    "df_forecast = pd.DataFrame({'Date':np.array(forecast_dates), 'Open':y_pred_future})\n",
    "df_forecast['Date']=pd.to_datetime(df_forecast['Date'])\n",
    "\n",
    "original = df[['Date', 'Open']]\n",
    "original['Date']=pd.to_datetime(original['Date'])\n",
    "original = original.loc[original['Date'] >= '2023-1-1'] # selecting the data only from 2023 just for our convinence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.lineplot(data=df_forecast, x='Date', y='Open', )\n",
    "sns.lineplot(data=original, x='Date', y='Open')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}