如何在使用LSTM进行预测时添加更多参数

Question

我应该在这段代码中做些什么更改，以便根据数据集上列出的所有参数预测输出，并预测第二天的开盘股价？你知道吗

当我试着运行时，它显示出一个变形的错误。此代码只使用一个参数就可以正常工作。你知道吗

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)

谢谢你。你知道吗