Forecasting and Granger Modelling with Non-linear Dynamical Dependencies

  • 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.

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Metadaten
Author:Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
DOI:https://doi.org/10.1007/978-3-319-71246-8_33
Parent Title (English):Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II
Editor:Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Document Type:Conference Proceeding
Language:English
Year of publication:2017
Release Date:2024/01/22
Volume:10535
Pages/Size:15
First Page:544
Last Page:558
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