Forecasting and Granger Modelling with Non-Linear Dynamical Dependencies
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.
Cite
Text
Gregorová et al. "Forecasting and Granger Modelling with Non-Linear Dynamical Dependencies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_33Markdown
[Gregorová et al. "Forecasting and Granger Modelling with Non-Linear Dynamical Dependencies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/gregorova2017ecmlpkdd-forecasting/) doi:10.1007/978-3-319-71246-8_33BibTeX
@inproceedings{gregorova2017ecmlpkdd-forecasting,
title = {{Forecasting and Granger Modelling with Non-Linear Dynamical Dependencies}},
author = {Gregorová, Magda and Kalousis, Alexandros and Marchand-Maillet, Stéphane},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {544-558},
doi = {10.1007/978-3-319-71246-8_33},
url = {https://mlanthology.org/ecmlpkdd/2017/gregorova2017ecmlpkdd-forecasting/}
}