如何将这种ConvLSTM形式的单变量时间序列预测转换为多变量时间序列预测？

Question

我是Keras的新手，正在尝试对多元数据执行多步预测。如何将对单变量数据进行预测以下的模型转换为对多变量数据进行预测的模型？

def model_fit(train, config):
    # unpack config
    n_seq, n_steps, n_filters, n_kernel, n_nodes, n_epochs, n_batch = config
    n_input = n_seq * n_steps
    # prepare data
    data = series_to_supervised(train, n_input)
    train_x, train_y = data[:, :-1], data[:, -1]
    train_x = train_x.reshape((train_x.shape[0], n_seq, 1, n_steps, 1))
    # define model
    model = Sequential()
    model.add(ConvLSTM2D(filters=n_filters, kernel_size=(1,n_kernel), activation='relu',
    input_shape=(n_seq, 1, n_steps, 1)))
    model.add(Flatten())
    model.add(Dense(n_nodes, activation='relu'))
    model.add(Dense(1))
    model.compile(loss='mse', optimizer='adam')
    # fit
    model.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)
    return model