The task of anomaly detection in data is one of the main challenges in data science because
of the wide plethora of applications and despite a spectrum of available methods …

The problem of finding anomalies and outliers in datasets is one of the most important
challenges of modern data analysis. Among the commonly dedicated tools to solve this task …

The term network surveillance is defined in general terms and illustrated with many
examples. Statistical methodologies that can be used as tools for network surveillance are …

Abstract We propose Fast Generalized Subset Scan (FGSS), a new method for detecting
anomalous patterns in general categorical data sets. We frame the pattern detection …

Anomaly detection in spatial time series (spatiotemporal data) is a challenging problem with
numerous potential applications. A comprehensive anomaly detection approach not only …

Spatial and time-dependent data is of interest in many applications. This task is difficult due
to its complex spatial dependency, long-range temporal dependency, data non-stationarity …

Objectives We investigate the potential for preventing crimes at temporary hot spots in
addition to chronic hot spots. Using data on serious violent crimes from Pittsburgh …

Spatial time series is a common type of data dealt with in many domains, such as economic
statistics and environmental science. There have been many studies focusing on finding and …

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection
on heterogeneous urban road networks based on link journey time (LJT) estimates …