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| HAL: hal-00683309, version 1 |
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| IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), Kyoto : Japon (2012) |
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| A Gaussian process regression approach for testing Granger causality between time series data |
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| Pierre-Olivier Amblard 1Olivier J.J. Michel 1 |
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| (2012-03-25) |
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| Granger causality considers the question of whether two time series exert causal influences on each other. Causality test- ing usually relies on prediction, i.e., if the prediction error of the first time series is reduced by taking measurements from the second one into account, then the latter is said to have a causal influence on the former. In this paper, a non-parametric framework based on functional estimation is proposed. Non- linear prediction is performed via the Bayesian paradigm, us- ing Gaussian processes. Some experiments illustrate the effi- ciency of the approach. |
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| 1: | Grenoble Images Parole Signal Automatique (GIPSA-lab) |
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CNRS : UMR5216 – Université Joseph Fourier - Grenoble I – Université Pierre-Mendès-France - Grenoble II – Université Stendhal - Grenoble III – Institut Polytechnique de Grenoble - Grenoble Institute of Technology |
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| 2: | Joseph Louis LAGRANGE (LAGRANGE) |
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Observatoire de la Côte d'Azur – Université Nice Sophia Antipolis [UNS] – CNRS : UMR7293 |
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| 3: | Institut Charles Delaunay (ICD-UTT) (ICD-UTT) |
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CNRS : FRE2848 – Université de Technologie de Troyes |
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| CICS |
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| Subject | : | Computer Science/Signal and Image Processing Engineering Sciences/Signal and Image processing |
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| Granger causality – functional estimation – Gaussian process – reproducing kernel |
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| hal-00683309, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00683309 | |
| oai:hal.archives-ouvertes.fr:hal-00683309 | |
| From: Olivier Michel | |
| Submitted on: Wednesday, 28 March 2012 14:06:51 | |
| Updated on: Monday, 28 January 2013 13:57:14 | |
