Anomaly Detection Using Support Vector Machines for Time Series Data
- 10.2991/jrnal.k.210521.010How to use a DOI?
- Anomaly detection; support vector machine; data mining; factory automation
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.
- © 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 - 2021/05/31 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 -