This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

Change points are abrupt variations in time series data. Such abrupt changes may represent
transitions that occur between states. Detection of change points is useful in modelling and …

Background The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted
the daily lives of millions. Beyond the general health repercussions of the pandemic itself …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing
attention to the potential impacts of AI technologies on society. In this paper we discuss one …

Object detection can be regarded as one of the most fundamental and challenging visual
recognition task in computer vision and it has received great attention over the past few …

Spectral decomposition of the Koopman operator is attracting attention as a tool for the
analysis of nonlinear dynamical systems. Dynamic mode decomposition is a popular …

This paper introduces a generic and scalable framework for automated anomaly detection
on large scale time-series data. Early detection of anomalies plays a key role in maintaining …

Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …

We address an anomaly detection setting in which training sequences are unavailable and
anomalies are scored independently of temporal ordering. Current algorithms in anomaly …