Journal of Robotics, Networking and Artificial Life

Volume 8, Issue 1, June 2021, Pages 41 - 46

Anomaly Detection Using Support Vector Machines for Time Series Data

Authors
Umaporn Yokkampon1, *, Sakmongkon Chumkamon1, Abbe Mowshowitz2, Ryusuke Fujisawa1, Eiji Hayashi1
1Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
2Department of Computer Science, The City College of New York, 160 Convent Avenue, New York, NY 10031, USA
*Corresponding author. Email: may@mmcs.mse.kyutech.ac.jp
Corresponding Author
Umaporn Yokkampon
Received 26 November 2020, Accepted 12 April 2021, Available Online 31 May 2021.
DOI
https://doi.org/10.2991/jrnal.k.210521.010How to use a DOI?
Keywords
Anomaly detection, support vector machine, data mining, factory automation
Abstract

Analysis of large data sets is increasingly important in business and scientific research. One of the challenges in such analysis stems from uncertainty in data, which can produce anomalous results. This paper proposes a method for detecting an anomaly in time series data using a Support Vector Machine (SVM). Three different kernels of the SVM are analyzed to predict anomalies in the UCR time series benchmark data sets. Comparison of the three kernels shows that the defined parameter values of the Radial Basis Function (RBF) kernel are critical for improving the validity and accuracy in anomaly detection. Our results show that the RBF kernel of the SVM can be used to advantage in detecting anomalies.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
8 - 1
Pages
41 - 46
Publication Date
2021/05
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.210521.010How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Umaporn Yokkampon
AU  - Sakmongkon Chumkamon
AU  - Abbe Mowshowitz
AU  - Ryusuke Fujisawa
AU  - Eiji Hayashi
PY  - 2021
DA  - 2021/05
TI  - Anomaly Detection Using Support Vector Machines for Time Series Data
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 41
EP  - 46
VL  - 8
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210521.010
DO  - https://doi.org/10.2991/jrnal.k.210521.010
ID  - Yokkampon2021
ER  -