Co-anomaly event is one of the most significant climate phenomena characterized by the co-occurrent similar abnormal patterns appearing in different temperature series. Indeed, these co-anomaly events play an important role in understanding the abnormal behaviors and natural disasters in climate research. However, to the best of our knowledge the problem of automatically detecting co-anomaly events in climate is still under-addressed due to the unique characteristics of temperature series data. To that end, in this paper we propose a novel framework Sevent for automatic detection of co-anomaly climate events in multiple temperature series. Specifically, we propose to first map the original temperature series to symbolic representations. Then, we detect the co-anomaly patterns by statistical tests and finally generate the co-anomaly events that span different sub-dimensions and subsequences of multiple temperature series. We evaluate our detection framework on a real-world data set which contains rich temperature series collected by 97 weather stations over 11 years in Hunan province, China. The experimental results clearly demonstrate the effectiveness of Sevent. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bai, X., Xiong, Y., Zhu, Y., Liu, Q., & Chen, Z. (2013). Co-anomaly event detection in multiple temperature series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8041 LNAI, pp. 1–14). Springer Verlag. https://doi.org/10.1007/978-3-642-39787-5_1
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