使用LSTM进行预测时如何添加更多参数

Question

我应该对代码进行哪些更改以根据数据集中列出的所有参数预测输出并预测第二天的开盘价？

当我尝试运行它时，显示了变形错误。只需1个参数，此代码即可正常工作。

我的代码如下：

dataset_train = pd.read_csv('ongc_train.csv')  
dataset_train = dataset_train.dropna()  
training_set = dataset_train.iloc[:, 1:2].value

# Creating a dataset with 60 timesteps and 1 output
X_train = []    
Y_train = []    
for i in range(60,2493):    
    X_train.append(training_set_scaled[i-60:i, 0])    
    Y_train.append(training_set_scaled[i, 0])       
X_train, Y_train = np.array(X_train), np.array(Y_train)         

# Reshaping
X_train  np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

# Fitting the RNN to the training set
regressor.fit(X_train, Y_train, epochs=100, batch_size=32)

# Getting the predicted stock price of 2017
# Concatenating the original training and test set
# Vertical concatenating of open stock prices
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis=0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 61):
    X_test.append(inputs[i-60:i, 0])
X_test=np.array(X_test)   
X_test=np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
actual = dataset_test.iloc[:, 1:2].values
print("Predicted Stock Price:",predicted_stock_price)

谢谢。