Multi-Source Time Series Data Prediction I was wondering if anyone has experience with time series prediction for data from multiple sources. So for instance, time series $a,b,..,z$ each have their own shape, some may be correlated with others. The ultimate goal is to have a model trained such that the value at time $t+1$ for any given data source can be predicted.

I personally have two solutions that in theory could work, but was wondering if anyone knew of other frequently used methods.


Multi-task learning with LSTM 
Use feature engineering to model properties of each time series source as features along with usual features and use these with LSTM
I'm only aware (and using) a RNN which gets multiple time-series in it's first layer and then mixes those in the following layers. Let me know if you have a question on this approach.