Probabilistic discovery of motifs in water level
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Nonlinear time series modeling and prediction using local variable weights RBF network
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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The application of SVR in the time series prediction is increasingly popular. Because some time series prediction based on SVR wasn't very nice in the efficiency of the forecast, this article presents a new regression based on linear regression and SVR. The new regression separates time series into linear part and nonlinear part, then predicts the two parts respectively, and finally integrates the two parts to forecast. Experiments show that the new regression advances the precision of the forecasting compared to the common SVR.