- Title
- A hybrid of multiobjective evolutionary algorithm and HMM-Fuzzy model for time series prediction
- Creator
- Hassan, Md Rafiul; Nath, Gupta; Kirley, Michael; Kamruzzaman, Joarder
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/35296
- Identifier
- vital:6210
- Identifier
-
https://doi.org/10.1016/j.neucom.2011.09.012
- Identifier
- ISSN:0925-2312
- Abstract
- In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.
- Relation
- Neurocomputing Vol. 81, no. April (2012), p. 1-11
- Rights
- Copyright Elsevier
- Rights
- This metadata is freely available under a CCO license
- Subject
- Fuzzy logic; Hidden Markov model; Time series; Prediction methods; 08 Information and Computing Sciences; 09 Engineering; 1701 Psychology
- Reviewed
- Hits: 2493
- Visitors: 2327
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|