Chand, Shelvin and Chandra, Rohitash (2014) Cooperative coevolution of feed forward neural networks for financial time series problem. [Conference Proceedings]
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Abstract
Intelligent financial prediction systems guide investors in making good investments. Investors are continuously on the hunt for better financial prediction systems. Neural networks have shown good results in the area of financial prediction. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This paper presents a computational intelligence framework for financial prediction where cooperative coevolutionary feedforward neural networks are used for predicting closing market prices for companies listed on the NASDAQ stock exchange. Problem decomposition is an important step in cooperative coevolution that affects its performance. Synapse and Neuron level are the main problem decomposition methods in cooperative coevolution. These two methods are used for training neural networks on the given financial prediction problem. The results show that Neuron level problem decomposition gives better
performance in general. A prototype of a mobile application is also given for investors that can be used on their Android devices.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences |
| Depositing User: | Rohitash Chandra |
| Date Deposited: | 26 May 2014 21:09 |
| Last Modified: | 11 Jul 2019 02:43 |
| URI: | https://repository.usp.ac.fj/id/eprint/7361 |
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