Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …

2 1 Einführung und weichen jeder Kollision elegant aus. Wieder andere folgen anscheinend
einem Führer. Auch aggressives Verhalten kann bei einigen beobachtet werden. Sehen wir …

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly
in the current era of smart devices, where enormous data are gathered from numerous …

This work addresses the outlier detection problem for feature-evolving streams, which has
not been studied before. In this setting both (1) data points may evolve, with feature values …

Unsupervised anomaly discovery in stream data is a research topic with many practical
applications. However, in many cases, it is not easy to collect enough training data with …

A massive amount of real-time and evolving streaming data are produced from various
devices and applications. Anomaly detection is one of the main tasks of streaming data …

Today smart meters are widely used in the energy sector to record energy consumption in
real time. Large amounts of smart meter data have been accumulated and used for diverse …