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Erschienen in:
A Novel Framework for Online Sales Burst Prediction Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Analyzing Granger Causality in Climate Data with Time Series Classification Methods

verfasst von : Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested.

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Vorheriges Kapitel A Novel Framework for Online Sales Burst Prediction
Nächstes Kapitel Automatic Detection and Recognition of Individuals in Patterned Species
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Metadaten
Titel
Analyzing Granger Causality in Climate Data with Time Series Classification Methods
verfasst von
Christina Papagiannopoulou
Stijn Decubber
Diego G. Miralles
Matthias Demuzere
Niko E. C. Verhoest
Willem Waegeman
Copyright-Jahr
2017
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71273-4_2

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