Learning coherent Granger-causality in panel vector autoregressive models

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) ; Marchand-Maillet, Stéphane ( Université de Genève, Suisse)

We consider the problem of forecasting multiple time series across multiple cross-sections based solely on the past observations of the series. We propose to use panel vector autoregressive model to capture the inter-dependencies on the past values of the multiple series. We restrict the panel vector autoregressive model to exclude the cross-sectional relationships and propose a method to learn models with sparse Granger-causality structures coherent across the panel sections. The method extends the concepts of group variable selection and support union recovery into the panel setting by extending the group lasso penalty (Yuan & Lin, 2006) into matrix output regression setting with 3d-tensor of model parameters.


Type de conférence:
short paper
Faculté:
Economie et Services
Ecole:
HEG - Genève
Institut:
CRAG - Centre de Recherche Appliquée en Gestion
Classification:
Informatique
Adresse bibliogr.:
Princeton , ICML
Date:
Princeton
ICML
2015
Pagination:
4 p.
Veröffentlicht in:
Proceedings of the Demand Forecasting Workshop of the 32nd International Conference on Machine Learning
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 Datensatz erzeugt am 2015-11-19, letzte Änderung am 2019-11-28

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