An improved multilayer perceptron based on wavelet approach for physical time series prediction
The real world datasets engage many challenges such as noisy data, periodic variations on several scales and long-term trends that do not vary periodically. Meanwhile, Neural Networks (NN) has been successfully applied in many problems in the domain of time series prediction. The standard NN a...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1454/1/24p%20ASHIKIN%20ALI.pdf http://eprints.uthm.edu.my/1454/2/ASHIKIN%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1454/3/ASHIKIN%20ALI%20WATERMARK.pdf |
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| Summary: | The real world datasets engage many challenges such as noisy data, periodic
variations on several scales and long-term trends that do not vary periodically.
Meanwhile, Neural Networks (NN) has been successfully applied in many problems
in the domain of time series prediction. The standard NN adopts computationally
intensive training algorithms and can easily get trapped into local minima. To
overcome such drawbacks in ordinary NN, this study focuses on using a wavelet
technique as a filter at the pre-processing part of the ordinary NN. However, this
study exposed towards an idea to develop a model called An Improved Multilayer
Perceptron based on Wavelet Approach for Physical Time Series Prediction (W�MLP) to overcome such drawbacks of ordinary NN. W-MLP, a network model with
a wavelet technique added in the network, is trained using the standard
backpropagation gradient descent algorithm and tested with historical temperature,
evaporation, humidity and wind direction data of Batu Pahat for 5-years-period
(2005-2009) and earthquake data of North California for 4-years-period (1995-1998).
Based on the obtained results, the proposed method W-MLP yields better
performance compared to the existing filtering techniques. Therefore, it can be
concluded that the proposed W-MLP can be an alternative mechanism to ordinary
NN for a one-step-ahead prediction of those five events. |
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