In recent years, there have been many time series methods proposed for forecasting enrollments, weather, the economy, population growth, and stock price, etc. However, traditional time series, such as ARIMA, expressed by mathematic equations are unable to be easily understood for stock investors. Besides, fuzzy time series can produce fuzzy rules based on linguistic value, which is more reasonable than mathematic equations for investors. Furthermore, from the literature reviews, two shortcomings are found in fuzzy time series methods: (1) they lack persuasiveness in determining the universe of discourse and the linguistic length of intervals, and (2) only one attribute (closing price) is usually considered in forecasting, not multiple attributes (such as closing price, open price, high price, and low price). Therefore, this paper proposes a multiple attribute fuzzy time series (FTS) method, which incorporates a clustering method and adaptive expectation model, to overcome the shortcomings above. In verification, using actual trading data of the Taiwan Stock Index (TAIEX) as experimental datasets, we evaluate the accuracy of the proposed method and compare the performance with the (Chen, 1996 , Yu, 2005 , and Cheng, Cheng, & Wang, 2008 ) methods. The proposed method is superior to the listing methods based on average error percentage (MAER). © 2009 Elsevier Ltd. All rights reserved.
Liu, J. W., Chen, T. L., Cheng, C. H., & Chen, Y. H. (2010). Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX. Computers and Mathematics with Applications, 59(2), 795–802. https://doi.org/10.1016/j.camwa.2009.10.014