Analyzing Granger causality in climate data with time series classification methods
- Author
- Christina Papagiannopoulou (UGent) , Stijn Decubber (UGent) , Diego Miralles (UGent) , Matthias Demuzere (UGent) , Niko Verhoest (UGent) and Willem Waegeman (UGent)
- Organization
- 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.
- Keywords
- Climate science, Attribution studies, Causal inference, Granger causality, Time series classification, NDVI DATA, REPRESENTATION, TEMPERATURE, SIMILARITY
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 508.90 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8552370
- MLA
- Papagiannopoulou, Christina, et al. “Analyzing Granger Causality in Climate Data with Time Series Classification Methods.” Lecture Notes in Artificial Intelligence, edited by Yasemin Altun et al., vol. 10536, Springer, 2017, pp. 15–26, doi:10.1007/978-3-319-71273-4_2.
- APA
- Papagiannopoulou, C., Decubber, S., Miralles, D., Demuzere, M., Verhoest, N., & Waegeman, W. (2017). Analyzing Granger causality in climate data with time series classification methods. In Y. Altun, K. Das, T. Mielikäinen, D. Malerba, J. Stefanowski, J. Read, … S. Džeroski (Eds.), Lecture Notes in Artificial Intelligence (Vol. 10536, pp. 15–26). https://doi.org/10.1007/978-3-319-71273-4_2
- Chicago author-date
- Papagiannopoulou, Christina, Stijn Decubber, Diego Miralles, Matthias Demuzere, Niko Verhoest, and Willem Waegeman. 2017. “Analyzing Granger Causality in Climate Data with Time Series Classification Methods.” In Lecture Notes in Artificial Intelligence, edited by Yasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka Žitnik, Michelangelo Ceci, and Sašo Džeroski, 10536:15–26. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-71273-4_2.
- Chicago author-date (all authors)
- Papagiannopoulou, Christina, Stijn Decubber, Diego Miralles, Matthias Demuzere, Niko Verhoest, and Willem Waegeman. 2017. “Analyzing Granger Causality in Climate Data with Time Series Classification Methods.” In Lecture Notes in Artificial Intelligence, ed by. Yasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka Žitnik, Michelangelo Ceci, and Sašo Džeroski, 10536:15–26. Cham, Switzerland: Springer. doi:10.1007/978-3-319-71273-4_2.
- Vancouver
- 1.Papagiannopoulou C, Decubber S, Miralles D, Demuzere M, Verhoest N, Waegeman W. Analyzing Granger causality in climate data with time series classification methods. In: Altun Y, Das K, Mielikäinen T, Malerba D, Stefanowski J, Read J, et al., editors. Lecture Notes in Artificial Intelligence. Cham, Switzerland: Springer; 2017. p. 15–26.
- IEEE
- [1]C. Papagiannopoulou, S. Decubber, D. Miralles, M. Demuzere, N. Verhoest, and W. Waegeman, “Analyzing Granger causality in climate data with time series classification methods,” in Lecture Notes in Artificial Intelligence, Skopje, Macedonia, 2017, vol. 10536, pp. 15–26.
@inproceedings{8552370,
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.}},
author = {{Papagiannopoulou, Christina and Decubber, Stijn and Miralles, Diego and Demuzere, Matthias and Verhoest, Niko and Waegeman, Willem}},
booktitle = {{Lecture Notes in Artificial Intelligence}},
editor = {{Altun, Yasemin and Das, Kamalika and Mielikäinen, Taneli and Malerba, Donato and Stefanowski, Jerzy and Read, Jesse and Žitnik, Marinka and Ceci, Michelangelo and Džeroski, Sašo}},
isbn = {{9783319712727}},
issn = {{0302-9743}},
keywords = {{Climate science,Attribution studies,Causal inference,Granger causality,Time series classification,NDVI DATA,REPRESENTATION,TEMPERATURE,SIMILARITY}},
language = {{eng}},
location = {{Skopje, Macedonia}},
pages = {{15--26}},
publisher = {{Springer}},
title = {{Analyzing Granger causality in climate data with time series classification methods}},
url = {{http://doi.org/10.1007/978-3-319-71273-4_2}},
volume = {{10536}},
year = {{2017}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: