Anomaly detection in diurnal data

In this paper we present methodological advances in anomaly detection tailored to discover abnormal traffic patterns under the presence of seasonal trends in data. In our setup we impose specific assumptions on the traffic type and nature; our study features VoIP call counts, for which several traces of real data has been used in this study, but the methodology can be applied to any data following, at least roughly, a non-homogeneous Poisson process (think of highly aggregated traffic flows). A performance study of the proposed methods, covering situations in which the assumptions are fulfilled as well as violated, shows good results in great generality. Finally, a real data example is included showing how the system could be implemented in practice.