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: [email protected]
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.210521.010
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 ALife Robotics Corp. Ltd.
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/).