Brunel University Research Archive (BURA) >
College of Engineering, Design and Physical Sciences >
Dept of Electronic and Computer Engineering >
Dept of Electronic and Computer Engineering Research Papers >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2393

Title: Using intelligent optimization methods to improve the group method of data handling in time series prediction
Authors: Abbod, MF
Dashpande, K
Keywords: GMDH
GA
PSO
Time series
Prediction
Finance
Publication Date: 2008
Publisher: Springer
Citation: The ICCS2008 International Conference on Computational Science: Advancing Science through Computation, Krakow, Poland, June 23-25, 2008
Abstract: In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm.
URI: http://bura.brunel.ac.uk/handle/2438/2393
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

Files in This Item:

File Description SizeFormat
Using Intelligent Optimization Methods to Improve the Group Method of Data Handling in Time Series Prediction.pdf151.74 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.