Research and development of a method to select the segmentation algorithm for the problem of detecting irregularities in time series data

Tóm tắt

Research and Enhancement of Segmentation Methods for Anomaly Detection in Time Series Data. This paper presents a study and improvement of segmentation techniques applied to the problem of anomaly detection in time series data. The work outlines the process of collecting and constructing essential datasets, including both periodic and non-periodic time series, along with a detailed description of the SWAT 2019 dataset. Subsequently, the paper delves into the segmentation process, focusing on the extraction of anomalous segments and the evaluation of experimental results. Finally, the study discusses the selection of the maximum allowable error (max_error), a critical parameter for optimizing the segmentation process and improving the performance of anomaly detection.