Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23525-7_45 | ECML/PKDD |
Keywords | Field | DocType |
Community detection,Spatiotemporal data,Climate index discovery,Seasonal rainfall prediction | Data mining,Graph,Predictability,Climate science,Computer science,Graph (abstract data type) | Conference |
Volume | ISSN | Citations |
9285 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gonzalo A. Bello | 1 | 2 | 0.39 |
| Michael P. Angus | 2 | 4 | 1.12 |
| Navya Pedemane | 3 | 2 | 0.39 |
| Jitendra K. Harlalka | 4 | 3 | 0.76 |
| Fredrick H. M. Semazzi | 5 | 23 | 3.42 |
| Vipin Kumar | 6 | 11560 | 934.35 |
| Nagiza F. Samatova | 7 | 861 | 74.04 |