Title :
Forecasting Models on Fuzzy Time Series Within Stock Market
Author :
Li San-ping ; Xu Cheng-Xian ; Xue Hong-Gang
Author_Institution :
Coll. of Math. & Inf. Sci., Shanxi Normal Univ., Xi´an, China
Abstract :
This article firstly presents an analysis and survey regarding the traditional evaluation and forecasting model on fuzzy time series. lt is pointed out that the maximum Subordination degree method and Subordination degree-Weighted average method is not suitable to attribute space usually, and a new evaluation model is proposed. The empirical study show that the new evaluation model is better able to evaluate and forecast the fuzzy time series within stock market.
Keywords :
forecasting theory; fuzzy set theory; stock markets; time series; forecasting models; fuzzy time series; maximum subordination degree method; stock market; subordination degree-weighted average method; Economic forecasting; Educational institutions; Fuzzy sets; Information analysis; Mathematical model; Mathematics; Predictive models; Space technology; Stock markets; Time series analysis;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5364855
