title: How can I implement the input of multiple regression in LSTM using keras?
body: <p>here is my code</p>
  
  <pre><code>def create_dataset(signal_data, look_back=1):
      dataX, dataY = [], []
      for i in range(len(signal_data) - look_back):
          dataX.append(signal_data[i:(i + look_back), 0])
          dataY.append(signal_data[i + look_back, 0])
      return np.array(dataX), np.array(dataY)
  
  df = pd.read_csv('time_series.csv')
  signal_data = df.Close.values.astype('float32')
  signal_data = signal_data.reshape(len(df), 1)
  
  
  scaler = MinMaxScaler(feature_range=(0, 1))
  signal_data = scaler.fit_transform(signal_data)
  
  train_size = int(len(signal_data) * 0.80)
  test_size = len(signal_data) - train_size)
  # val_size = len(signal_data) - train_size - test_size
  train = signal_data[0:train_size]
  # val = signal_data[train_size:train_size+val_size]
  test = signal_data[train_size+val_size:len(signal_data)]
  
  x_train, y_train = create_dataset(train, look_back)
  # x_val, y_val = create_dataset(val, look_back)
  x_test, y_test = create_dataset(test, look_back)
  
  
  x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
  # x_val = np.reshape(x_val, (x_val.shape[0], x_val.shape[1], 1))
  x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
  </code></pre>
  
  <p>now I want to add <code>df.Open</code> and <code>df.High</code> and  <code>df.Low</code> and <code>df.Volume</code></p>
  
  <p>how can I implement this code?</p>
  
  <p>Should I just add to the signal data? I'm wondering how to add data so that I can train multiple features in the signal data.</p>
  
  <p>I don't know where and how to implement it. I need your help.</p>
  
  <p>Your valuable opinions and thoughts will be very much appreciated.</p>