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Analyzing Granger causality in climate data with time series classification methods

Christina Papagiannopoulou (UGent) , Stijn Decubber (UGent) , Diego Miralles (UGent) , Matthias Demuzere (UGent) , Niko Verhoest (UGent) and Willem Waegeman (UGent)
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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.
Keywords
Climate science, Attribution studies, Causal inference, Granger causality, Time series classification, NDVI DATA, REPRESENTATION, TEMPERATURE, SIMILARITY

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MLA
Papagiannopoulou, Christina, Stijn Decubber, Diego Gonzalez Miralles, et al. “Analyzing Granger Causality in Climate Data with Time Series Classification Methods.” Lecture Notes in Artificial Intelligence. Ed. Yasemin Altun et al. Vol. 10536. Cham, Switzerland: Springer, 2017. 15–26. Print.
APA
Papagiannopoulou, Christina, Decubber, S., Gonzalez 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, M. Žitnik, et al. (Eds.), Lecture Notes in Artificial Intelligence (Vol. 10536, pp. 15–26). Presented at the Joint European conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2017), Cham, Switzerland: Springer.
Chicago author-date
Papagiannopoulou, Christina, Stijn Decubber, Diego Gonzalez 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. 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.
Chicago author-date (all authors)
Papagiannopoulou, Christina, Stijn Decubber, Diego Gonzalez 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. 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.
Vancouver
1.
Papagiannopoulou C, Decubber S, Gonzalez 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. Gonzalez 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://dx.doi.org/10.1007/978-3-319-71273-4_2},
  volume       = {10536},
  year         = {2017},
}

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