Research Article
Segmentation-Based Methods for Top-kDiscords Detection in Static and Streaming Time Series Under Euclidean Distance
@INPROCEEDINGS{10.1007/978-3-030-93179-7_12, author={Huynh Thi Thu Thuy and Duong Tuan Anh and Vo Thi Ngoc Chau}, title={Segmentation-Based Methods for Top-kDiscords Detection in Static and Streaming Time Series Under Euclidean Distance}, proceedings={Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={ICCASA}, year={2022}, month={1}, keywords={Anomaly detection Top-k discords Segmentation Clustering Streaming time series}, doi={10.1007/978-3-030-93179-7_12} }- Huynh Thi Thu Thuy
Duong Tuan Anh
Vo Thi Ngoc Chau
Year: 2022
Segmentation-Based Methods for Top-kDiscords Detection in Static and Streaming Time Series Under Euclidean Distance
ICCASA
Springer
DOI: 10.1007/978-3-030-93179-7_12
Abstract
Detecting top-kdiscords in time series is more useful than detecting the most unusual subsequence since the result is a more informative and complete set, rather than a single subsequence. The first challenge of this task is to determine the length of discords. Besides, detecting top-kdiscords in streaming time series poses another challenge that is fast response when new data points arrive at high speed. To handle these challenges, we propose two novel methods, TopK-EP-ALeader and TopK-EP-ALeader-S, which combine segmentation and clustering for detecting top-kdiscords in static and streaming time series, respectively. Moreover, a circular buffer is built to store the local segment of a streaming time series and calculate anomaly scores efficiently. Along with this circular buffer, a delayed update policy is defined for achieving instant responses to overcome the second challenge. The experiments on nine datasets in different application domains confirm the effectiveness and efficiency of our methods for top-kdiscord discovery in static and streaming time series.
