Abstract
The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-k spatial outliers over several time periods. The top-k spatial outliers are found using the Exact-Grid Top- k and Approx-Grid Top- k algorithms, which are an extension of algorithms developed by Agarwal et al. [1]. Since they use the Kulldorff spatial scan statistic, they are capable of discovering all outliers, unaffected by neighbouring regions that may contain missing values. After generating the outlier sequences, we show one way they can be interpreted, by comparing them to the phases of the El Niño Southern Oscilliation (ENSO) weather phenomenon to provide a meaningful analysis of the results.
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Wu, E., Liu, W., Chawla, S. (2010). Spatio-temporal Outlier Detection in Precipitation Data. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_7
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DOI: https://doi.org/10.1007/978-3-642-12519-5_7
Publisher Name: Springer, Berlin, Heidelberg
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