数据流上异常数据的在线检测与修正

给出了带有遗忘因子改进的Kalman滤波预测算法,能够检测未来时刻的异常数据;提出了一种新颖的数据流上的异常数据修正方法,应用插值小波根据连续异常数据数量的不同,实现了可变插值尺度的异常数据修补,能够自适应修正精度.在实际电力负荷数据上的仿真实验证明这种方法可以在线准确地检测到异常数据,并能提供精确的异常数据修正.

Detection and Repairing Method for Outliers over Data Streams

Outlier detection and repairing in a data stream environment are discussed.An online detection method for outliers over data streams,called amnesia Kalman filtering(AKF),is proposed.In order to identify outliers,an improved Kalman filter is applied with the amnesia factor to forecast data-value in the future timestamp.A novel online adaptive repairing method for outliers over data streams,called adaptive interpolating wavelet(AdaptiveIW),is then proposed.The AdaptiveIW applies a method of variable-resolution interpolation,named the interpolation wavelet with adaptive resolution,to repair outliers,which determines interpolation resolution based on the number of continuous outliers.It adapts to different requested precision for outlier repairing over evolving data streams very well.Experimental results on actual power load data show that this method can provide precise instantaneous detection and accurate repairing for outliers over data streams.