Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the slidin...Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the sliding window mean value （PLR_MTSW） algorithm is proposed. With this algorithm, the mutation points can be identified accurately according to the rate Of mean value change, while the main features of time series are maintained well. This algorithm can also smooth the noise and improve the compression accuracy with sliding window. Meanwhile the local extreme points can be identified effectively according to the change of mean value trend within window.展开更多
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Natural Science Foundation of Shanghai Municipality(Grant No.08ZR1408400)
文摘Seismic data show some important characteristics, such as big volume and strong timeliness. Specific to the time series data of earthquake precursory observations, a piecewise linear representation based on the sliding window mean value （PLR_MTSW） algorithm is proposed. With this algorithm, the mutation points can be identified accurately according to the rate Of mean value change, while the main features of time series are maintained well. This algorithm can also smooth the noise and improve the compression accuracy with sliding window. Meanwhile the local extreme points can be identified effectively according to the change of mean value trend within window.
