Supervised compression of multivariate time series data

A problem of supervised learning from the multivariate time series (MTS) data where the target variable is potentially a highly complex function of MTS features is considered. This paper focuses on finding a compressed representation of MTS while preserving its predictive potential. Each time sequence is decomposed into Chebyshev polynomials, and the decomposition coefficients are used as predictors in a statistical learning model. The feature selection method capable of handling true multivariate effects is then applied to identify relevant Chebyshev features. MTS compression is achieved by keeping only those predictors that are pertinent to the response.