本研究使用類神經模糊系統並結合差分自回歸移動平均模型(ARIMA)與混合式學習法，提出一個創新性的計算智慧方法之於時間序列預測。依據上述方法，本研究設計出NFS-ARIMA模型做為一個適應性預測系統。在NFS-ARIMA預測系統中，重點在於模糊系統的設計並將差分自回歸移動平均模型應用到模糊法則的後鑑部。本研究使用兩種系統結構學習法來產生模糊法則並進行比較，分別為格狀法與自我組織分群法。在自我組織分群法中，透過輸入資料的分佈，NFS-ARIMA預測系統使用模糊C平均分裂演算法進行自我建構與學習以建立模糊法則。此外本研究結合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，粒子群最佳化演算法用來調整模糊法則的前鑑部參數，至於遞迴最小平方估計法則用來調整後鑑部參數。本研究使用5個實驗範例來測試本系統的預測效能，透過實驗結果可證明本系統的預測準確性，且透過PSO-RLSE混合式學習法，NFS-ARIMA預測系統在訓練時可以達到極快的收斂速度並且具有極佳的預測效能，實驗1與實驗2的結果同時呈現本系統的預測準確度優於大部分其他文獻所提出的方法與模型並且有效提升預測準確度。而本系統也能針對現實世界中經濟與財務領域相關的時間序列，例如股票與國際匯率有不錯的預測效能與精準度。

A new computational intelligence approach to the problem of time series forecasting is proposed, using a Neuro-Fuzzy System (NFS), Auto-Regressive Integrated Moving Average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFS-ARIMA model, which is used as an adaptive predictor to the problem of time series forecasting. For the NFS-ARIMA, the focus is on the formation of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. Two rule generation methods including grid-type and cluster-based self-organizing method are used to make comparison in system structure learning. For the cluster-based method, the NFS-ARIMA can learn its initial knowledge base from training data. With the mechanism of self-organization, fuzzy rules are generated by clusters using the FCM-based splitting algorithm (FBSA). For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined in hybrid way so that they can update the free parameters of the NFS-ARIMA predictor in efficient way. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. Five examples are used to test the proposed approach for forecasting ability. Through the experimental results, the proposed approach shows its excellence in prediction accuracy. With the hybrid PSO-RLSE learning method, the learning process for the NFS-ARIMA predictor can converge in fast pace, and the prediction accuracy is admirable. The results of Example 1 and 2 by the proposed approach are compared to other approaches. The performance comparison shows the proposed approach performs appreciably better than many compared approaches. The NFS-ARIMA forecasting approach is also applied to real-world applications of stock price and foreign exchange rate.

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