Abstract
This work proposes a method of combining wavelet transforms to feed forward artificial neural networks for ATM (Asynchronous Transfer Mode) traffic prediction. Wavelet transforms are used to preprocess the nonlinear time- series in order to provide a step-closer phase learning paradigm to the artificial neural network. The network uses a variable length time window on approximation coefficients over all scales. It was observed that this approach could improve the generalization ability as well as the accuracy of the artificial neural network for ATM traffic prediction.
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© 2001 Springer-Verlag Berlin Heidelberg
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Barreto, P.S., Lemos, R.P. (2001). ATM Traffic Prediction Using Artificial Neural Networks and Wavelet Transforms. In: Networking — ICN 2001. ICN 2001. Lecture Notes in Computer Science, vol 2094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47734-9_66
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DOI: https://doi.org/10.1007/3-540-47734-9_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42303-4
Online ISBN: 978-3-540-47734-1
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