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Many network resource management solutions typically employ traffic prediction algorithms to improve the performance of a network. In this paper, the authors extend a newly developed method for prediction with confidence to time series data and apply it to the network traffic demand prediction problem. They investigate the performance of the proposed algorithm on a number of publicly available network traffic demand datasets. The experimental results are very promising. In this paper, they consider the problem of network traffic demand prediction, i.e., given a set of previous traffic demand observations in a network, they want to predict the traffic amount (e.g., source-destination demand) for the next period of time (e.g., hour).
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