Augmenting Granger Causality through continuous ordinal patterns
We here propose a novel methodology, based on the concept of continuous ordinal patterns, to preprocess time series and make explicit the non-linear temporal structures in them present. Through a series of synthetic and real-world examples, we show how such transformation overcomes one major limitat...
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/365072 |
| Acesso em linha: | http://hdl.handle.net/10261/365072 https://api.elsevier.com/content/abstract/scopus_id/85173825722 |
| Access Level: | acceso abierto |
| Palavra-chave: | Time series Causality Granger Causality test Ordinal patterns |
| Resumo: | We here propose a novel methodology, based on the concept of continuous ordinal patterns, to preprocess time series and make explicit the non-linear temporal structures in them present. Through a series of synthetic and real-world examples, we show how such transformation overcomes one major limitation of the celebrated Granger Causality test, and allows to efficiently detect non-linear causality relations without the need of a priori assumptions. We further show how such transformation can be optimised based on the time series under study; but that good results can also be achieved using random ordinal patterns, in a way similar to how randomness is exploited in Reservoir Computing. We finally discuss the complementarity between this approach and the standard Granger one, especially in the analysis of real-world, and hence unknown, causal relations. |
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