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- W2535119999 abstract "The accuracy of network traffic prediction has received significant interest in various domains, such as capacity planning, anomaly detection, admission control, and traffic engineering. For large-time scale traffic variation, it shows both a daily pattern and an hour pattern, which means the model based on single trend has not met the needs of prediction. Therefore, by dealing with the internal relationship of large-time scale network traffic, this paper combines the regular trend and the smooth or seasonal trend of hours and days, then fit the dual-related model to predict large-time scale traffic. The result indicates that the proposed model effectively identified the correlations of data between days and hours, and is successful in forecasting approaches." @default.
- W2535119999 created "2016-10-28" @default.
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- W2535119999 date "2016-08-01" @default.
- W2535119999 modified "2023-09-27" @default.
- W2535119999 title "Forecasting network traffic at large time scale by using dual-related method" @default.
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- W2535119999 doi "https://doi.org/10.1109/fskd.2016.7603372" @default.
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