I am interested in time-series forecasting with RandomForest. The basic approach is to use a rolling window and use the data points within the window as features for the RandomForest regression, where we regress the next values after the window on the values within the window. Just plain autoregressive model (with lags), but with Random Forest instead of linear regression.
If I have more than one time-series (multiple time-series), how to pass them in RF regression?
For example: given two time series $y_1(t)$ and $y_2(t)$, the outcome time series is $z(t)$ and I am interested in predicting the values of $z(t)$ based on the combination of $y_1$ and $y_2$.
What I need, is to use rolling window for each $y_1$ and $y_2$, and then feed these values within the window from both time-series into RF regression, to predict the value of $z(t)$.
How do I incorporate the data from both rolling windows into the input for RF regression?