Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Anomaly detection has gained considerable attention in the past couple of years. Emerging
technologies, such as the Internet of Things (IoT), are known to be among the most critical …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

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 …

With ongoing automation and digitization of the electric power system, several Phasor
Measurement Units (PMUs) have been deployed for monitoring and control. PMU data can …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can
prevent massive damage to devices or resources, which may then lead to catastrophic …

Lifelong learning addresses the challenge of acquiring new knowledge and tackling new
tasks in a continually evolving environment. Although this thread of research has recently …

Multivariate time series anomaly detection is a widespread problem in the field of failure
prevention. Fast prevention means lower repair costs and losses. The amount of sensors in …

Outliers are unexpected observations, which deviate from the majority of observations.
Outlier detection and prediction are challenging tasks, because outliers are rare by …