A fuzzy identification procedure for nonlinear time series: With example on ARCH and bilinear models
✍ Scribed by Berlin Wu; Shu-Lin Hung
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 131 KB
- Volume
- 108
- Category
- Article
- ISSN
- 0165-0114
No coin nor oath required. For personal study only.
✦ Synopsis
In this paper we propose a detecting scheme for nonlinear time-series model classiÿcation by using a knowledge-based construction and fuzzy statistical decision making. ARCH and bilinear models are frequently applied in economic or ÿnancial time-series modeling, and both models exhibit certain kind of pattern similarity, such as unusual jumps and a diversity trend. So we take these two models as our illustration example for demonstration. Simulation results presented here demonstrate that our detecting procedure can classify ARCH and bilinear models e ectively. The designed detection process also exhibits a signiÿcant rate of correct recognition for sunspot series and exchange rates.