Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311–319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609–624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481–491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.