智慧型區間預測之研究─以複數模糊類神經、支持向量迴歸、拔靴統計為方法; A Study for Interval Forecasting – An Intelligent Approach Using Complex Neuro-Fuzzy System, Support Vector Regression and Bootstrap

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题名:	智慧型區間預測之研究─以複數模糊類神經、支持向量迴歸、拔靴統計為方法;A Study for Interval Forecasting – An Intelligent Approach Using Complex Neuro-Fuzzy System, Support Vector Regression and Bootstrap
作者:	林傳維;Chuan-Wei Lin
贡献者:	資訊管理研究所
关键词:	遞迴最小平方估計法;模糊分群;匯率時間序列;複數模糊集合;複數模糊類神經系統;支持向量迴歸;粒子群最佳化演算法;exchange rate time series.;FCM-based clustering;support vector regression (SVR);particle swarm optimization (PSO);recursive least squares estimator (RLSE);complex neuro-fuzzy system (CNFS);complex fuzzy sets
日期:	2011-07-28
上传时间:	2012-01-05 15:12:50 (UTC+8)
摘要:	本研究使用複數模糊類神經系統 (Complex neuro-fuzzy system, CNFS) 結合支持向量迴歸 (Support vector regression, SVR) 為方法，設計一個新穎的複數模糊類神經支持向量迴歸預測器 (Complex neuro-fuzzy system based SVR, CNFS-SVR)，並結合移動區塊拔靴法作時間序列的區間預測之研究。複數模糊類神經系統使用複數模糊集合，複數模糊集合擁有優秀的適應能力與非線性映射能力，且複數模糊類神經系統的輸出是屬於複數值，預測值的實部與虛部可以用於同時預測兩個不同的時間序列。支持向量迴歸理論基於結構風險最小化 (Structural risk minimization, SRM) 原則設計以達到優秀的一般化能力。模糊類神經系統作為支持向量迴歸器的映射函數，如此複數模糊類神經支持向量迴歸預測器經由模糊類神經系統與支持向量迴歸器的協同作用來達到系統優異的預測性能。在系統學習階段中，模糊C平均分裂演算法可以自動產生適當的規則數作為系統架構，並使用粒子群最佳化演算法與遞迴最小平方估計法，形成複合式學習法進行系統參數學習。根據移動區塊拔靴法與該預測器來建立信賴區間對時間序列作區間預測。本研究使用各種不同的匯率作為研究對象，其結果顯示複數模糊類神經支持向量迴歸預測器結合移動區塊拔靴法能同時預測兩個時間序列且期預測性能有不錯的表現。 A novel intelligent approach using complex neuro-fuzzy system based support vector regression (denoted as CNFS-SVR) and moving-block bootstrap is proposed to the problem of time series interval forecasting in this thesis. The proposed CNFS-SVR approach combines both of the complex neuro-fuzzy system (CNFS) theory and the support vector regression (SVR) theory. With complex fuzzy sets (CFSs), the CNFS has excellent adaptive ability for functional mapping. The output of CNFS is complex-valued and can be used to develop the so-called dual output capability, which can be used to predict two time series simultaneously. SVR is based on the statistical learning theory. With the principle of structural risk minimization (SRM), SVR can possess excellent generalization ability without over fitting. In the study, CNFS-SVR is developed to integrate the merits of the CNFS theory and the SVR rationale to obtain excellent performance. Bootstrap is a re-sampling method, by which empirical statistical distribution can be developed and confidence interval can be obtained using statistical inference. For the learning strategy, a FCM-based clustering method is used to automatically determine the initial knowledge base of CNFS-SVR. Particle swarm optimization (PSO) and recursive least squares estimator (RLSE) algorithm are used in a hybrid way to update the parameters of If-Then fuzzy rules of CNFS-SVR. The LibSVM package is used to optimize the proposed CNFS-SVR machines. Several real-world exchange-rate time series are used in the study. The experimental results show promising performance.
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