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.
| 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 |
