Spatio-temporal Outlier Detection in Precipitation Data

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 previous data mining techniques, new techniques to detect spatio-temporal outliers need to be developed. In this paper, we propose a spatio-temporal outlier detection algorithm called Outstretch. To apply this algorithm, we first need to discover the top-k outliers (high discrepancy regions) for each time period. For this task, we have extended the Exact-Grid and Approx-Grid algorithms developed by Agarwal et al. [2] to develop ExactGrid Top-k and Approx-Grid Top-k. One advantage of these algorithms is that they use the Kulldorff spatial scan statistic, which is able to calculate a valid discrepancy value that allows the discovery of all the outliers, unaffected by neighbouring regions that may contain missing values. After generating the 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.