LSTM时间序列会产生变化的预测吗？

Question

我正在使用LSTM NN和Keras进行时间序列预测。作为输入要素，有两个变量（降水量和温度），要预测的一个目标是地下水位。

尽管实际数据和输出之间存在严重偏差，但似乎工作得很好（参见图片）。

现在我已经读到，这可能是网络无法正常工作的经典迹象，因为它似乎在模仿输出和

  

模型实际上在做的是在预测   时间“ t + 1”，它只是将时间“ t”的值用作其预测https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424

但是，在我的情况下这实际上是不可能的，因为目标值未用作输入变量。我正在使用具有两个功能的多变量时间序列，与输出功能无关。 而且，预测值在将来（t + 1）不会偏移，而似乎会落后（t-1）。

有人知道什么可能导致此问题吗？

这是我的网络的完整代码：

# Split in Input and Output Data 
x_1 = data[['MeanT']].values
x_2 = data[['Precip']].values
y = data[['Z_424A_6857']].values

# Scale Data
x = np.hstack([x_1, x_2])
scaler = MinMaxScaler(feature_range=(0, 1))
x = scaler.fit_transform(x)

scaler_out = MinMaxScaler(feature_range=(0, 1))
y = scaler_out.fit_transform(y)

# Reshape Data
x_1, x_2, y = H.create2feature_data(x_1, x_2, y, window)
train_size = int(len(x_1) * .8)
test_size = int(len(x_1)) #  * .5

x_1 = np.expand_dims(x_1, 2) # 3D tensor with shape (batch_size, timesteps, input_dim) // (nr. of samples, nr. of timesteps, nr. of features)
x_2 = np.expand_dims(x_2, 2)
y = np.expand_dims(y, 1)

# Split Training Data
x_1_train = x_1[:train_size]
x_2_train = x_2[:train_size]
y_train = y[:train_size]

# Split Test Data
x_1_test = x_1[train_size:test_size]
x_2_test = x_2[train_size:test_size]
y_test = y[train_size:test_size]

# Define Model Input Sets
inputA = Input(shape=(window, 1))
inputB = Input(shape=(window, 1))

# Build Model Branch 1
branch_1 = layers.GRU(16, activation=act, dropout=0, return_sequences=False, stateful=False, batch_input_shape=(batch, 30, 1))(inputA)
branch_1 = layers.Dense(8, activation=act)(branch_1)
#branch_1 = layers.Dropout(0.2)(branch_1)
branch_1 = Model(inputs=inputA, outputs=branch_1) 

# Build Model Branch 2
branch_2 = layers.GRU(16, activation=act, dropout=0, return_sequences=False, stateful=False, batch_input_shape=(batch, 30, 1))(inputB)
branch_2 = layers.Dense(8, activation=act)(branch_2)
#branch_2 = layers.Dropout(0.2)(branch_2)
branch_2 = Model(inputs=inputB, outputs=branch_2) 

# Combine Model Branches
combined = layers.concatenate([branch_1.output, branch_2.output])

# apply a FC layer and then a regression prediction on the combined outputs
comb = layers.Dense(6, activation=act)(combined)
comb = layers.Dense(1, activation="linear")(comb)

# Accept the inputs of the two branches and then output a single value
model = Model(inputs=[branch_1.input, branch_2.input], outputs=comb)
model.compile(loss='mse', optimizer='adam', metrics=['mse', H.r2_score])

model.summary()

# Training
model.fit([x_1_train, x_2_train], y_train, epochs=epoch, batch_size=batch, validation_split=0.2, callbacks=[tensorboard])
model.reset_states()

# Evaluation
print('Train evaluation')
print(model.evaluate([x_1_train, x_2_train], y_train))

print('Test evaluation')
print(model.evaluate([x_1_test, x_2_test], y_test))

# Predictions
predictions_train = model.predict([x_1_train, x_2_train])
predictions_test = model.predict([x_1_test, x_2_test])

predictions_train = np.reshape(predictions_train, (-1,1))
predictions_test = np.reshape(predictions_test, (-1,1))

# Reverse Scaling
predictions_train = scaler_out.inverse_transform(predictions_train)
predictions_test = scaler_out.inverse_transform(predictions_test)

# Plot results
plt.figure(figsize=(15, 6))
plt.plot(orig_data, color='blue', label='True GWL')  
plt.plot(range(train_size), predictions_train, color='red', label='Predicted GWL (Training)')
plt.plot(range(train_size, test_size), predictions_test, color='green', label='Predicted GWL (Test)')
plt.title('GWL Prediction')  
plt.xlabel('Day')  
plt.ylabel('GWL')  
plt.legend()  
plt.show()   

我正在使用30个时间步的批处理大小，回溯90个时间步的批处理，总数据大小约为7500个时间步。

任何帮助将不胜感激:-)谢谢！