Modeling principles in fuzzy time series forecasting


Duru O., Yoshida S.

IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr), New-York, United States Of America, 29 - 30 March 2012, pp.18-24 identifier

  • Publication Type: Conference Paper / Full Text
  • City: New-York
  • Country: United States Of America
  • Page Numbers: pp.18-24

Abstract

Fuzzy time series forecasting is one of the most applied extensions of the fuzzy set theory. Since it is first introduced by Song and Chissom [1,2], several improvements are indicated by many scholars and its practical popularity increases gradually. While the FTS methods are applied for many different problems, fundamental drawbacks are found in the existing literature. The stationarity problem, non-linear dataset and identification of initial fuzzy intervals are some of the debated topics in the FTS research. This paper discusses the principles of the FTS modeling and deals with the common mistakes in the literature.