Learning Simple Non Stationarities with Hyper Parameters
We consider sequential data that is sampled from an unknown process so that the data are not necessarily i i d Most approaches to machine learning assume that data points are i i d Instead we consider a measure of generalization that does not make this assumption and we consider in this context a recently proposed approach to opti mizing hyper parameters based on the computation of the gradient of a model selection criterion with respect to hyper parameters Here we use hyper parameters that con trol a function that gives di erent weights to di erent time steps in the historical data sequence The approach is successfully applied to modeling the volatility of stock re turns one month ahead Comparative experiments with more traditional methods are presented