A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

Pengcheng Zhang, Yan Xiao, Yuelong Zhu, Jun Feng, Dingsheng Wan, Wenrui Li and Hareton Leung
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Pengcheng Zhang: College of Computer and Information, Hohai University, Nanjing, China
Yan Xiao: College of Computer and Information, Hohai University, Nanjing, China
Yuelong Zhu: College of Computer and Information, Hohai University, Nanjing, China
Jun Feng: College of Computer and Information, Hohai University, Nanjing, China
Dingsheng Wan: College of Computer and Information, Hohai University, Nanjing, China
Wenrui Li: College of Computer and Information, Hohai University, Nanjing, China & School of Mathematics and Information Technology, Nanjing Xiaozhuang University, Nanjing, China
Hareton Leung: Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 3, 26-45

Abstract: Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD_DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.

Date: 2016
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