NDLI: Cooperative coevolution of feed forward neural networks for financial time series problem

Content Provider	IEEE Xplore Digital Library
Author	Chand, S. Chandra, R.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput., Inf. & Math. Sci., Univ. of the South Pacific, Suva, Fiji (Chand, S.; Chandra, R.)
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 co-evolution 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.
Starting Page	202
Ending Page	209
File Size	633927
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889568
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Time series analysis Biological neural networks Mobile communication Training Smart phones Companies
Content Type	Text
Resource Type	Article

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