Keras - 使用LSTM进行模式预测

Question

对于我的实验，我有一个格式化的CSV file有三列（time_stamp，X和Y - 其中Y是实际值）。我想根据过去值的时间指数预测Y的X值。正如您在下图中所看到的那样，使用机器学习回归的模式预测似乎运行良好。

我想使用深度学习技术（例如使用LSTM）重现此预测（绘图），这就是我使用Keras来解决问题的方法。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.preprocessing.sequence import TimeseriesGenerator
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

np.random.seed(7)

df = pd.read_csv('test32_C_data.csv')
n_features=10

values = df.values

for i in range(0,n_features):
    df['X_t'+str(i)] = df['X'].shift(i) 
    df['X_tp'+str(i)] = (df['X'].shift(i) - df['X'].shift(i+1))/(df['X'].shift(i))

print(df)
pd.set_option('use_inf_as_null', True)

#df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)
df.dropna(inplace=True)

X = df.drop('Y', axis=1)
y = df['Y']


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)

X_train = X_train.drop('time', axis=1)
X_train = X_train.drop('X_t1', axis=1)
X_train = X_train.drop('X_t2', axis=1)
X_test = X_test.drop('time', axis=1)
X_test = X_test.drop('X_t1', axis=1)
X_test = X_test.drop('X_t2', axis=1)


sc = MinMaxScaler()

X_train = np.array(df['X'])
X_train = X_train.reshape(-1, 1)
X_train = sc.fit_transform(X_train)

y_train = np.array(df['Y'])
y_train=y_train.reshape(-1, 1)
y_train = sc.fit_transform(y_train)


model_data = TimeseriesGenerator(X_train, y_train, 100, batch_size = 10) 

# Initialising the RNN
model = Sequential() 

# Adding the input layerand the LSTM layer
model.add(LSTM(4, input_shape=(None, 1)))

# Adding the output layer
model.add(Dense(1)) 

# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop') 

# Fitting the RNN to the Training set
model.fit_generator(model_data)

# evaluate the model
scores = model.evaluate(X_train, y_train)
#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

# Getting the predicted values
predicted = X_test
predicted = np.reshape(-1, 1)
predicted = sc.transform(predicted)
y_pred = model.predict(predicted)
y_pred = sc.inverse_transform(y_pred)

但是，当我尝试运行如上所示的预测模型（y_pred = model.predict(predicted)）时 - 我收到以下错误。

ValueError: Error when checking: expected lstm_2_input to have 3 dimensions, but got array with shape (1, 1)

我在这里做错了什么？我将不胜感激任何帮助。

Answer 1

LSTM输入的等级为3：

• 批量大小：在您的情况下 1 ，但任何值都可以使用。
• 时间步骤：模型接受任何值，因此 1 会执行此操作，但更长的输入序列通常会带来更好的预测。
• 功能：在模型中显示为 1 。

因此，您应该将predicted重塑为：

predicted = predicted.values.reshape((-1, 1, 1))