A Timescale Decomposition Approach to Network Traffic Prediction

Guoqiang MAO

doi:10.1093/ietcom/e88-b.10.3974

A Timescale Decomposition Approach to Network Traffic Prediction

Guoqiang MAO

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The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the à trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.

Publication: IEICE TRANSACTIONS on Communications Vol.E88-B No.10 pp.3974-3981

Publication Date: 2005/10/01

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DOI: 10.1093/ietcom/e88-b.10.3974

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

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Guoqiang MAO, "A Timescale Decomposition Approach to Network Traffic Prediction" in IEICE TRANSACTIONS on Communications, vol. E88-B, no. 10, pp. 3974-3981, October 2005, doi: 10.1093/ietcom/e88-b.10.3974.
Abstract: The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the à trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e88-b.10.3974/_p

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@ARTICLE{e88-b_10_3974,
author={Guoqiang MAO, },
journal={IEICE TRANSACTIONS on Communications},
title={A Timescale Decomposition Approach to Network Traffic Prediction},
year={2005},
volume={E88-B},
number={10},
pages={3974-3981},
abstract={The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the à trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.},
keywords={},
doi={10.1093/ietcom/e88-b.10.3974},
ISSN={},
month={October},}

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TY - JOUR
TI - A Timescale Decomposition Approach to Network Traffic Prediction
T2 - IEICE TRANSACTIONS on Communications
SP - 3974
EP - 3981
AU - Guoqiang MAO
PY - 2005
DO - 10.1093/ietcom/e88-b.10.3974
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E88-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2005
AB - The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the à trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.
ER -