Anomaly Detection Using Support Vector Machines for Time Series Data

DOI

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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TY  - JOUR
AU  - Umaporn Yokkampon
AU  - Sakmongkon Chumkamon
AU  - Abbe Mowshowitz
AU  - Ryusuke Fujisawa
AU  - Eiji Hayashi
PY  - 2021
DA  - 2021/05/31
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  - 10.2991/jrnal.k.210521.010
ID  - Yokkampon2021
ER  -