LSTM模型预测结果不佳

原文标题 ：LSTM model has poor prediction results

#根据气象数据和历史径流预测未来径流

Streamflow=pd.read_csv('###.csv', delimiter=',')
x = Streamflow.drop('Q',axis=1)   
Y = Streamflow['Q']               
X = np.array(x)                   
y = np.array(Y)
            
test_size = int(len(X) * 0.15)
valid_size = int(len(X) * 0.15)
train_size= len(X) - (valid_size+test_size)
y_train, y_valid, y_test = y[0:train_size], y[train_size:train_size+valid_size], y[-test_size:]
X_train, X_valid, X_test = X[0:train_size], X[train_size:train_size+valid_size], X[train_size+valid_size:]

X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))  
X_valid = np.reshape(X_valid, (X_valid.shape[0], 1, X_valid.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
input=X_train[1:]
input_shape=X_train.shape[1:]
print (y_train.shape, y_valid.shape, y_test.shape)

model = Sequential()
model.add(LSTM(150, input_shape=X_train.shape[1:], activation='relu',return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(300, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.6))
model.add(Dense(1, activation='relu'))                                     

optimizer = tf.keras.optimizers.SGD(learning_rate=e_LR) 
model.compile(optimizer=optimizer,loss='MeanAbsoluteError')  
history = model.fit(X_train, y_train, epochs=e_epoch, batch_size=e_batch_size, verbose=0, validation_data=(X_valid, y_valid), shuffle=True)

在此处输入图像描述这是模型损失和结果。训练期和验证期的结果都还可以，但是测试期的结果太差了。我应该如何修改模型？（数据未归一化，因为归一化的预测是一条直线。）