I came across some practical problems with the data in similar form:
Traditionally, data for predictive models in textbook is only
item*features, and we use features to make prediction. item are usually iid.
I am confused with this kind(
item*features*time) of data at first until somebody told me that this is called panel data. This remind me that I saw this concept in some econometrics book. However, most econometrics are linear models.
Given that we have tons of predictive models today(like LASSO, RT, GBM, SVM, even deep learning), my question is that is there any way to build predictive models for panel data? Are there any good references?
Some practical fact of the data:
It is not i.i.d, so observations with nearer time may have some correlations, or some relationship, which we may make use of in the predictive model. Also, sometimes, the items are related, the relationship between the items may also need to take into consideration.
Can I force to make this 3-D data into 2-D, by melting
item*featuresas one long observations, so that the data becomes
observation*features, which is in traditional text book shape? Will the result for this good or meaningful?