Brief Report
Version 1
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Anomaly Detection over Time Series Data
Version 1
: Received: 26 July 2022 / Approved: 26 July 2022 / Online: 26 July 2022 (10:50:27 CEST)
How to cite: Zhu, Z. Anomaly Detection over Time Series Data. Preprints 2022, 2022070407. https://doi.org/10.20944/preprints202207.0407.v1 Zhu, Z. Anomaly Detection over Time Series Data. Preprints 2022, 2022070407. https://doi.org/10.20944/preprints202207.0407.v1
Abstract
The anomaly detection task is very important in computer science. And there are a lot of anomaly detection methods. Different from some thresholding methods, some unsupervised methods could make us get more accurate and faster result, which is the object of the project. In this paper, I tried to use EWMA and some other methods in two datasets: Webank time consuming indicators dataset and AIOps Challenge dataset. The paper consists nine parts: background of the project, related work, description of algorithms, implementation details, experimental setup and data sets used, experimental results and discussion, future directions, reference and meeting notes.
Keywords
Anomaly Detection ; Time Series; EWMA
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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