A Novel Approach to the Similarity Analysis of Multivariate Time Series and Its Application in Hydrological Data Mining

There has been large amount of hydrological data collected by various sensors during the last years and how to discover the hidden knowledge among these data has caused more and more attention from diverse fields, such as hydrologist and researchers from data mining. This paper deals with similarity mining from hydrological time series and concentrates itself on the similarity analysis of multivariate time series (MTS). A novel similarity measure has been put forward, which is based on the well-known BORDA count in multiple classifier system. Firstly, dimension reduction is adaptively conducted according to the target data complexity; then the similarity of single time series is computed and lastly, the overall similarity of the MTS is obtained by synthesizing each of the single similarity based on BORDA count. Experiments on the similarity analysis of water level data of TAIHU Lake and historical flood data from YIFENG Basin have shown the feasibility and effectiveness of the proposed method.