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• W2726765978 abstract "Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques." @default.
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• W2726765978 date "2017-01-01" @default.
• W2726765978 modified "2023-09-24" @default.
• W2726765978 title "Forecasting and Granger Modelling with Non-linear Dynamical Dependencies" @default.
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• W2726765978 doi "https://doi.org/10.1007/978-3-319-71246-8_33" @default.
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