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Abstract
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Article Information:
Firefly Algorithm with Artificial Neural Network for Time Series Problems
Mohammed Alweshah
Corresponding Author: Mohammed Alweshah
Submitted: December 01, 2013
Accepted: January 24, 2014
Published: May 15, 2014 |
Abstract:
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Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN). The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the Firefly Algorithm (FA) with the ANN in order to minimize the error rate of classification (coded as FA-ANN). The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems.
Key words: Artifitail neural networks, firefly algorithm, time series problems, , , ,
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Cite this Reference:
Mohammed Alweshah, . Firefly Algorithm with Artificial Neural Network for Time Series Problems. Research Journal of Applied Sciences, Engineering and Technology, (19): 3978-3982.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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