I'm trying to design a DNN for time series prediction. I have time series data with 2 features. I would like to leverage on stacked LSTM layers due to it's powerful predicting capability. However I'm not sure how to combine the 2 features and still use stacked LSTM model.
I think the first layer is 2 separate LSTMs, one for feature1 and one for feature2. I would like to merge the 2 output somehow and feed it to the next layer, the stacked LSTM. So it would be something like this:

Input1; Input2

LSTM_1(Feature1); LSTM_2(Feature2)

Combine LSTM_1 and LSTM_2 output

LSTM_3

I have the following questions:
In general do I think in the right direction to use both features with LSTM?
What is the common way for the step of combining, if any? Average? Sum?

I'm trying to design a DNN for time series prediction. I have time series data with 2 features. I would like to leverage on stacked LSTM layers due to it's powerful predicting capability. However I'm not sure how to combine the 2 features and still use stacked LSTM model.
I think the first layer is 2 separate LSTMs, one for feature1 and one for feature2. I would like to merge the 2 output somehow and feed it to the next layer, the stacked LSTM. So it would be something like this:

Input1; Input2
LSTM_1(Feature1); LSTM_2(Feature2)
Combine LSTM_1 and LSTM_2 output
LSTM_3

I have the following questions:
In general do I think in the right direction to use both features with LSTM?
What is the common way for the step of combining, if any? Average? Sum?