Time series input selection using multiple kernel learning

In this paper we study the relevance of multiple kernel learn- ing (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear com- bination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for opti- mizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simu- lated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).