For network administration and maintenance, it is critical to anticipate when
networks will receive peak volumes of traffic so that adequate resources can be
allocated to service requests made to servers. In the event that sufficient
resources are not allocated to servers, they can become prone to failure and
security breaches. On the contrary, we would waste a lot of resources if we
always allocate the maximum amount of resources. Therefore, anticipating peak
volumes in network traffic becomes an important problem. However, popular
forecasting models such as Autoregressive Integrated Moving Average (ARIMA)
forecast time-series data generally, thus lack in predicting peak volumes in
these time-series. More than often, a time-series is a combination of different
features, which may include but are not limited to 1) Trend, the general
movement of the traffic volume, 2) Seasonality, the patterns repeated over some
time periods (e.g. daily and monthly), and 3) Noise, the random changes in the
data. Considering that the fluctuation of seasonality can be harmful for trend
and peak prediction, we propose to extract seasonalities to facilitate the peak
volume predictions in the time domain. The experiments on both synthetic and
real network traffic data demonstrate the effectiveness of the proposed method