N-order difference heuristic model of fuzzy time series forecasting

Author

Chi Kai ; Che Wen-Gang

Author_Institution

Pattern Recognition & Intell. Syst., Kunming Univ. of Sci. & Technol., Kunming, China

Volume

2

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

358

Lastpage

361

Abstract

Fuzzy time series forecasting model is an effective method to solve the nonlinear problems forecasting. However, most published fuzzy time series based models did not count the change trend implicit in historical datum. In this paper, authors proposed a novel method which applied heuristic information to the fuzzy time series model based on Fibonacci sequence. As an example, the USD/JPY exchange rate is tested in this model. The results show that this method not only improves the forecasting accuracy, but decreases the computational complexity.

Keywords

Fibonacci sequences; forecasting theory; fuzzy set theory; time series; Fibonacci sequence; N-order difference heuristic model; fuzzy time series forecasting model; fuzzy time series model; heuristic information; nonlinear problems forecasting; Automation; Channel hot electron injection; Economic forecasting; Fuzzy systems; Intelligent systems; Laboratories; Pattern recognition; Predictive models; Technology forecasting; Uncertainty; Forecasting; Fuzzy relations; Fuzzy time series; Heuristic models; N-order difference; USD/JPY;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5358380

Filename

5358380