Using Intelligent Optimization Methods to Improve the Group Method of Data Handling in Time Series Prediction

Authors:
Maysam Abbod;Karishma Deshpande
Affiliations:
School of Engineering and Design, Brunel University, West London, UK Uxbridge, UK UB8 3PH;School of Engineering and Design, Brunel University, West London, UK Uxbridge, UK UB8 3PH
Venue:
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Year:
2008

Citing 5
Cited 1

The fuzzy GMDH algorithm by possibility models and its application

Fuzzy Sets and Systems
Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets

Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

An evolutionary-based hyper-heuristic approach for optimal construction of group method of data handling networks

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

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.