Title :
Nonlinear prediction on maximum timings of complex time series
Author :
Kanno, Yoshitaka ; Ikeguchi, T.
Author_Institution :
Dept. of Inf. & Comput. Sci., Saitama Univ., Japan
Abstract :
We have already proposed a nonlinear modeling method which uses event sizes and event timings. In this paper, we consider availability of our method for continuous time series by predicting occurrence timing of maxima and their sizes from continuous time series. In order to evaluate availability of our scheme, we introduce the prediction accuracy by the following two methods. The first one is to predict continuous time series, using all information of the continuous time series. The second is to extract maxima from continuous time series and apply our proposed modeling scheme to the maxima of time series. Comparing these results, we show that our method has higher predictability if there exists an underlying dynamics of observed complex behavior.
Keywords :
continuous time systems; nonlinear systems; reliability; time series; complex time series; continuous time series; event sizes; event timings; maximum timings; nonlinear modeling method; nonlinear prediction; observed complex behavior; occurrence timing; prediction accuracy; Accuracy; Availability; Chaos; Data mining; Equations; Information analysis; Prediction algorithms; Predictive models; Time series analysis; Timing;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1201914