小编Num*_*ind的帖子

我的数据框是每小时一次（我的 df 的索引），我想预测 y。

> df.head()

          Date           y             
    2019-10-03 00:00:00 343   
    2019-10-03 01:00:00 101  
    2019-10-03 02:00:00 70  
    2019-10-03 03:00:00 67  
    2019-10-03 04:00:00 122  

我现在将导入库并训练模型：

  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  from sklearn.preprocessing import MinMaxScaler
  min_max_scaler = MinMaxScaler()
  prediction_hours = 24
  df_train= df[:len(df)-prediction_hours]
  df_test= df[len(df)-prediction_hours:]
  print(df_train.head())
  print('/////////////////////////////////////////')
  print (df_test.head())
  training_set = df_train.values
  training_set = min_max_scaler.fit_transform(training_set)

  x_train = training_set[0:len(training_set)-1]
  y_train = training_set[1:len(training_set)]
  x_train = np.reshape(x_train, (len(x_train), 1, 1))
  num_units = 2
  activation_function = 'sigmoid'
  optimizer = …