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.

翻译：在网络管理与维护中，准确预测网络流量达到峰值的时间至关重要，以便为服务器处理的服务请求分配充足资源。若服务器资源分配不足，则易导致故障和安全漏洞；反之，若始终分配最大资源量，则会造成资源过度浪费。因此，预测网络流量峰值成为重要课题。然而，自回归积分滑动平均模型(Autoregressive Integrated Moving Average, ARIMA)等常用预测模型仅能对时间序列数据进行通用预测，难以有效捕获其中的峰值特征。通常，时间序列由多种特征组合而成，包括但不限于：1)趋势——流量总体变化方向；2)季节性——按特定周期(如日、月)重复出现的模式；3)噪声——数据中的随机波动。考虑到季节性波动可能干扰趋势预测与峰值识别，本文提出通过提取季节性成分来增强时域中的流量峰值预测能力。基于合成数据与真实网络流量数据的实验验证了所提方法的有效性。