Fuzzy time series analysis method based on least squares support vector machines
Shen Bin, Wensheng Yi · Journal of Zhejiang University(Engineering Science) · 2005
Aiming at the problem of low precision of traditional fuzzy time series (FTS) analysis methods, this work proposed a new method based on least squares support vector machines (LS-SVM), which is an efficient tool for pattern recognition and regression estimation. This method includes two parts. In the FTS processing part, establish heuristic rules and fuzzy variables and determine the universe of discourse, fuzzy sets and membership degree functions, then fuzzify the history data. The LS-SVM processing part uses LS-SVM instead of traditional fuzzy relationship computation to analyze fuzzy data, and produces the final results after defuzzification. Comparing with traditional FTS analysis methods, this new method can obtain higher accuracy and good generalization quality.