A Survey of Methods for Time Series Change Point Detection


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 prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

Nov 8, 2023 1:30 PM — 2:00 PM
EMIL Fall'23 Seminars
Health Futures Center, ASU
Abdullah Mamun
Abdullah Mamun
Graduate Research Associate

Currently I am working on building and optimizing deep learning models for time-series data.