ML Anthology

Analyzing Granger Causality in Climate Data with Time Series Classification Methods

Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman
ECML-PKDD 2017 pp. 15-26
doi:10.1007/978-3-319-71273-4_2 /ecmlpkdd/2017/papagiannopoulou2017ecmlpkdd-analyzing/

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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Text

Papagiannopoulou et al. "Analyzing Granger Causality in Climate Data with Time Series Classification Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_2

Markdown

[Papagiannopoulou et al. "Analyzing Granger Causality in Climate Data with Time Series Classification Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/papagiannopoulou2017ecmlpkdd-analyzing/) doi:10.1007/978-3-319-71273-4_2

BibTeX

@inproceedings{papagiannopoulou2017ecmlpkdd-analyzing,
  title     = {{Analyzing Granger Causality in Climate Data with Time Series Classification Methods}},
  author    = {Papagiannopoulou, Christina and Decubber, Stijn and Miralles, Diego G. and Demuzere, Matthias and Verhoest, Niko E. C. and Waegeman, Willem},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2017},
  pages     = {15-26},
  doi       = {10.1007/978-3-319-71273-4_2},
  url       = {https://mlanthology.org/ecmlpkdd/2017/papagiannopoulou2017ecmlpkdd-analyzing/}
}