Hi class, I found I didn’t really get the concept of forward-looking and backward-looking, and here I have some summary from our class materials. Please point out if anything is incorrect, and I’m glad to hear any understanding on this.
For Forward-looking methods:
- entire dataset is used (searched) and models look into both future and historic values; (I read an article saying that forward-looking only uses data that should be unchanged from now on, but I wonder what is some data that would change through time and can be used in modeling and training? Is this saying right? Also I wonder what is future value in this case?)
- fitting transformation/interacting/mapping on training data and then only apply on testing data;
- perform on rolling basis and extracting the appropriate values;
For Backward-looking methods:
- recursive and only takes present and historical values into account;
- directly fit on all the data and we don’t have to distinguish between train and test data;
- recalculated at every time step to form a time series of data points; (here I didn’t get the difference between this point with rolling bases for forward-looking)
Thanks for any explanation!