Abstract | ||
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Prediction on complex time series has received much attention during the last decade. This paper reviews least square and radial basis function based predictors and proposes a support vector regression (SVR) based local predictor to improve phase space prediction of chaotic time series by combining the strength of SVR and the reconstruction properties of chaotic dynamics. The proposed method is applied to Henon map and Lorenz flow with and without additive noise, and also to Sunspots time series. The method provides a relatively better long term prediction performance in comparison with the others. |
Year | DOI | Venue |
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2008 | 10.1016/j.patcog.2007.08.013 | Pattern Recognition |
Keywords | Field | DocType |
local prediction,henon map,chaotic dynamic,long term prediction performance,support vector regression,lorenz flow,sunspots time series,non-linear time series,phase space prediction,chaotic time series,additive noise,complex time series,radial basis function,least square,state space,time series,time series analysis,phase space | Time series,Order of integration,Radial basis function network,Radial basis function,Long-term prediction,Hénon map,Support vector machine,Artificial intelligence,Chaotic,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
41 | 5 | Pattern Recognition |
Citations | PageRank | References |
34 | 1.94 | 8 |
Authors | ||
2 |