A hybrid predictor for time series prediction
Yen-Ping Chen, Sheng‐Nan Wu, Jeen-Shing Wang · 2005
This paper presents a hybrid predictor for the CATS (competition on artificial time series) benchmark. Considering the time series as a sum of two components: the major trend and a residual series, we tackled the prediction problem by a hybrid predictor consisting of two models - a kernel regression model and a recurrent neuro-fuzzy model. The kernel regression model based on Gaussian function expansions was first applied to predict the major trend of the time series. The time series was sectioned into several data sets to obtain the best-fitting regression model. Subsequently, the recurrent neuro-fuzzy model associated with a learning algorithm was used to predict the dynamics of the residual series. The learning algorithm has been developed to construct a minimum size of the recurrent model in state-space representation. The best prediction results were presented and discussed.