Temporal feature selection for time-series prediction

Shohei Hido, Tetsuro Morimura

Abstract: We present a feature selection method for multivariate time-series prediction. It aims to use the best sliding window size and delay for each explanatory variable, which are usually fixed. The idea is to convert the original time-series into a set of cumulative sum with different length. The combinations of cumulative sum variables obtaining nonzero weights in sparse learning algorithms represent the optimal temporal effects from explanatory variables to the target variable. Experiments show that the method performs better than conventional methods in regression problems.