INTRODUCTION Many animals live and interact as part of social groups. Social groups
present unique advantages to their members, but also necessitate viable modes of …

The behavior of a time series may be affected by various factors. Changes in mean,
variance, frequency, and auto-correlation are the most common. Change-Point Detection …

Pain is a dynamic, complex and multidimensional experience. The identification of pain from
brain activity as neural readout may effectively provide a neural code for pain, and further …

In recent years, there have been a large number of proposed approaches to detecting
changes in mean. A natural question for an analyst is which method is most appropriate for …

Identifying change/break points in multivariate time series represents a canonical problem in
signal processing, due to numerous applications related to anomaly detection problems …

In the field of covert data integrity attacks, considerable attention has focused on two
important issues. One is the issue of how to change the state of a plant, and the other is how …

The present study aims at estimating the multiple change points for the time series data of
COVID-19 confirmed cases and deaths and trend estimation within the estimated multiple …

Functional magnetic resonance imaging (fMRI) time series data presents a unique
opportunity to understand temporal brain connectivity, and models that uncover the complex …

Sequential change-point detection from time series data is a common problem in many
neuroscience applications, such as seizure detection, anomaly detection, and pain …

We study the problem of detecting a common change point in large panel data based on a
mean shift model, wherein the errors exhibit both temporal and cross-sectional dependence …