Functional learning of time-series models preserving Granger-causality structures

Gregorova, Magda ( Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Kalousis, Alexandros ( Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Dinuzzo, Francesco ( Amazon)

We develop a functional learning approach to modelling systems of time series which preserves the ability of standard linear time-series models (VARs) to uncover the Granger-causality links in between the series of the system while allowing for richer functional relationships. We propose a framework for learning multiple output-kernels associated with multiple input-kernels over a structured input space and outline an algorithm for simultaneous learning of the kernels with the model parameters with various forms of regularization including non-smooth sparsity inducing norms. We present results of synthetic experiments illustrating the benefits of the described approach.


Conference Type:
short paper
Faculty:
Economie et Services
School:
HEG - Genève
Institute:
CRAG - Centre de Recherche Appliquée en Gestion
Subject(s):
Informatique
Publisher:
Montréal, Canada , 11th December 2015
Date:
Montréal, Canada
11th December 2015
2015
Pagination:
5 p.
Published in
Proceedings of the Time Series Workshop of the 29th Neural Information Processing Systems conference, NIPS-2015
External resources:
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 Record created 2016-08-22, last modified 2019-04-23

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