The application of Series Importance Points (SIP) based partition method on hydrological data processing

Abstract

China has accumulated a large amount of valuable hydrological data, and the descriptive physical variables can be categorized into various types of hydrological time series. Time-series data usually contains huge amounts of high-dimension data that being continuously updated, thus it is difficult to directly mine the original time series data. This chapter adopts a time series segmentation algorithm based on series importance point (SIP)-PLR-SIP, to approximately describe time series with line segments based on SIP. SIPs are used as the splitting point to reflect the main features of time series and reduce the dimensions of the time series data, thus minimizing the overall error. © 2012 Springer Science+Business Media B.V.