混合複數類神經模糊與自動回歸差分平均移動方法之-電子商務學報.PDFVIP

第十五卷　第一期　2013 年3 月 （pp.137～158 ） * CNFS ARIMA CNFS-ARIMA CNFS-ARIMA CNFS NFS Intelligent Time Series Forecasting Model Combining Complex Neuro-Fuzzy Computing Model and ARIMA Method Chunshien LiTai-Wei Chiang Department of Information Management, National Central University, Taiwan Abstract A complex neuro-fuzzy system, using complex fuzzy sets (CFSs), neuro-fuzzy theory, and autoregressive integrated moving average (ARIMA) model, is proposed to the problem of time series forecasting. The proposed computing system is denoted as CNFS-ARIMA. To update the free parameters of the proposed CNFS-ARIMA, a novel hybrid learning * 通訊作者　電子信箱：j amesli@.tw NSC99-222 1-E-008-088 March 2013 137 method is devised, combining both the particle swarm optimization (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm . The PSO is used to adj ust the premise parameters of the proposed predictor, and the RLSE is used to update the consequent parameters. To test the proposed approach, two benchmark time series datasets are used. The experimental results by the proposed approach are compared with those by its neuro-fuzzy counterpart and by other approaches in literatures. The experimental results have illustrated the merits of CFSs in the proposed approach with excellent performance for the two examples of time series forecasting. Throu