In this paper we study the relevance of multiple kernel learning (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
combination of basis kernels, exploiting different sources of information.
An efficient algorithm wrapping a Support Vector Regression model
for optimizing 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 simulated linear and nonlinear time series (AutoRegressive,
Henon and Lorenz series).