USP Theses Collection


close this section of the library Chand, Shelvin

View the PDF document Multi-objective cooperative neuro-evolution for chaotic time series prediction
Author:Chand, Shelvin
Institution: University of the South Pacific.
Award: M.Sc.
Subject: Neural networks (Computer science), Time-series analysis
Date: 2014
Call No.: Pac QA 76 .87 .C53 2014
BRN: 1198119
Copyright:Under 10% of this thesis may be copied without the authors written permission

Abstract: The use of neural networks for time series prediction has been an important focus of recent research. Multi-objective optimization techniques have been used for training neural networks for time series prediction in the past. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This thesis presents a multi-objective cooperative coevolutionary method for training neural networks for time series prediction where the time series data sets are preprocessed to obtain the dierent objectives. Data sets with dierent time lags are used as the objectives to be optimized. The method is tested on benchmark data sets including both real world and simulated time series problems. The results show that the multi-objective approach is able to improve the overall prediction accuracy while using one generalized neural network for predicting data sets representing dierent time-lags. A prototype of a mobile application for nancial prediction is also given for potential investors to use on their Android based mobile devices.
Disclaimer & Copyright l Contact Us l
© Copyright 1968 - 2018. All Rights Reserved.
USP Library
The University of the South Pacific
Laucala Campus, Suva, Fiji
Tel: +679 323 1000