Sallehuddin, Roselina and Shamsuddin, Siti Mariyam and Mustafa, Noorfa Haszlinna (2012) Enhancing forecasting performance of multivariate time series using new hybrid feature selection. In: Communications in Computer and Information Science. Springer-Verlag, Berlin, pp. 373-380. ISBN 978-364231836-8
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Official URL: http://dx.doi.org/10.1007/978-3-642-31837-5_54
Abstract
The aim of this study is to propose a new hybrid feature selection model to improve the performance of multivariate time series (MTS) forecasting under uncertainty situation. This new hybrid model is called cooperative feature selection (CFS) and consists of two different component; GRA Analyzer and ANN Optimizer. The performance of CFS is evaluated on KLSE close price. The statistical analysis of the results shows that CFS has high ability to recognize and remove irrelevant input for obtaining optimum input factors, shortening the learning time and improving forecasting accuracy for vague MTS.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | ccuracy, ANN optimizer, cooperative feature selection, GRA analyzer, multivariate time series |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Computer Science and Information System |
| ID Code: | 35761 |
| Deposited By: | INVALID USER |
| Deposited On: | 11 Nov 2013 09:54 |
| Last Modified: | 04 Feb 2017 06:01 |
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