Online prediction method for data streams based on wavelet synopses
Guo Ji-ping · Jisuanji gongcheng · 2005
Several studies in recent years were demonstrated that wavelets can be efficiently used to compress large quantities of data down to compact wavelet synopses and provided fast and fairly accurate approximate answers to queries. In this paper author presented a wavelet synopses and prediction framework to analyze dynamic high-frequency data streams. A novel construction method for wavelet synopses provided with efficient De-noise ability was proposed. Its varied threshold schema for every decomposition level could adapt itself to the change of background noise. Based on this wavelet synopsis, a multi-scale prediction and analysis method for summarization was used to separate out the trend, turning points, cyclical fluctuations and autocorrelational effects etc. This framework was used to provide annotation for time series data streams at real-time. Experimental results with real power load datasets demonstrate that our approach achieves improved velocity and accuracy to approximate prediction queries when compared to existing techniques.