Discovering climate indices–time series that summarize spatiotemporal climate patterns–is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability. Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.
|Original language||English (US)|
|Title of host publication||Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015|
|Editors||Vitor Santos Costa, Carlos Soares, Annalisa Appice, Annalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, João Gama, Alípio Jorge, Pedro Pereira Rodrigues, João Gama, Vitor Santos Costa, Alípio Jorge, Annalisa Appice, Pedro Pereira Rodrigues, João Gama, Annalisa Appice, Carlos Soares, Alípio Jorge, João Gama, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, Alípio Jorge|
|Number of pages||16|
|ISBN (Print)||9783319235240, 9783319235240, 9783319235240, 9783319235240|
|State||Published - 2015|
|Event||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal|
Duration: Sep 7 2015 → Sep 11 2015
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015|
|Period||9/7/15 → 9/11/15|
Bibliographical noteFunding Information:
This material is based upon work supported in part by the Laboratory for Analytic Sciences, the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, and NSF grant 1029711.
© Springer International Publishing Switzerland 2015.
- Climate index discovery
- Community detection
- Seasonal rainfall prediction
- Spatiotemporal data