Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
| Autor: | Alexandros Kalousis, Magda Gregorova, Stéphane Marchand-Maillet |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Multivariate statistics Series (mathematics) Computer science 02 engineering and technology Function (mathematics) Space (mathematics) Set (abstract data type) 03 medical and health sciences Nonlinear system 030104 developmental biology Kernel (statistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing ddc:025.063 Algorithm Reproducing kernel Hilbert space |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II pp. 544-558 Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712451 Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2017, Proceedings, Part II Lecture Notes in Computer Science Lecture Notes in Computer Science-Machine Learning and Knowledge Discovery in Databases |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.1007/978-3-319-71246-8_33 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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