APForecast: an adaptive forecasting method for data streams

Authors:
Yong-li Wang;Hong-bing Xu;Yi-sheng Dong;Xue-jun Liu;Jiang-bo Qian
Affiliations:
Department of Computer Science and Engineering, Southeast University, Nanjing, China;Department of Computer Science and Engineering, Southeast University, Nanjing, China;Department of Computer Science and Engineering, Southeast University, Nanjing, China;Department of Computer Science and Engineering, Southeast University, Nanjing, China;Department of Computer Science and Engineering, Southeast University, Nanjing, China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

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Abstract

This research investigates continuous forecasting queries with alterable forecasting-step over data streams. A novel Adaptive Precision forecasting method to forecasting single attribute value of item in a single steam (stream-value), called AForecast, is proposed. The concepts of dual slide windows and forecasting-steps conduction operator are introduced. AForecast determines multiple forecasting-step based on the change ratio of stream-value and forecasts random-variant stream-value using relative precise prediction of deterministic components of data streams. Based on the theory of interpolating wavelet and optimal linear kalman filtering, this method can approximately generate optimal forecasting precision. Experiment results on actual power load data prove that this method can provide online accurate prediction on stream-value.