Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

The Internet of Things (IoT) is a fact today where a high number of nodes are used for
various applications. From small home networks to large-scale networks, the aim is the …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …

Outlier detection is an important task in data mining, with applications ranging from intrusion
detection to human gait analysis. With the growing need to analyze high speed data …