Wong Wai-Keung, Bai Enjian, Chu Alice Wai-Ching
Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong-Kong.
IEEE Trans Syst Man Cybern B Cybern. 2010 Dec;40(6):1531-42. doi: 10.1109/TSMCB.2010.2042055. Epub 2010 Mar 22.
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.
模糊时间序列已应用于入学人数、温度、股票指数及其他领域的预测。相关研究主要集中在三个因素上，即话语划分、预测规则的内容以及去模糊化方法，所有这些因素都极大地影响预测模型的预测准确性。这些研究使用固定的分析窗口大小进行预测。本文提出了一种自适应时变模糊时间序列预测模型（ATVF）以提高预测准确性。所提出的模型在训练阶段根据预测准确性自动调整模糊时间序列的分析窗口大小，并在测试阶段使用启发式规则生成预测值。使用模拟和实际时间序列对ATVF模型的性能进行了测试，包括阿拉巴马大学塔斯卡卢萨分校的入学人数以及台湾证券交易所加权股价指数（TAIEX）。实验结果表明，与其他模糊时间序列预测模型相比，所提出的ATVF模型在预测准确性方面有显著提高。
