This survey article discusses methods for detecting change points in time series data. Change points are abrupt changes in time series data that may indicate transitions between states. The article reviews various supervised and unsupervised algorithms for detecting change points, compares them based on cost, limitations, and performance, and identifies challenges in the field. It also introduces criteria for evaluating these algorithms, including accuracy, sensitivity, precision, F-measure, and ROC and PR curves. The article covers different approaches to change point detection, including likelihood ratio methods, subspace models, probabilistic methods, kernel-based methods, graph-based methods, and clustering methods. Each method is discussed in terms of its application, performance, and suitability for different types of time series data. The article concludes with a discussion of the challenges and future directions for research in change point detection.This survey article discusses methods for detecting change points in time series data. Change points are abrupt changes in time series data that may indicate transitions between states. The article reviews various supervised and unsupervised algorithms for detecting change points, compares them based on cost, limitations, and performance, and identifies challenges in the field. It also introduces criteria for evaluating these algorithms, including accuracy, sensitivity, precision, F-measure, and ROC and PR curves. The article covers different approaches to change point detection, including likelihood ratio methods, subspace models, probabilistic methods, kernel-based methods, graph-based methods, and clustering methods. Each method is discussed in terms of its application, performance, and suitability for different types of time series data. The article concludes with a discussion of the challenges and future directions for research in change point detection.