Umaporn Yokkampon1, Abbe Mowshowitz2, Sakmongkon Chumkamon1, 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
pp. 206-210
ABSTRACT
Uncertainty is ubiquitous in data and constitutes a challenge in real-life
data analysis applications. To deal with this challenge, we propose a novel
method for detecting anomalies in time series data based on the Autoencoder
method, which encodes a multivariate time series as images by means of
the Gramian Angular Summation Field (GASF). Multivariate time series data
is represented as 2D image data to enhance the performance of anomaly detection.
The proposed method is validated with four time-series data sets. Experimental
results show that our proposed method can improve validity and accuracy
on all criteria. Therefore, effective anomaly detection in multivariate
time series data can be achieved by combining the methods of Autoencoder
and Gramian Angular Summation Field.
ARTICLE INFO
Article History
Received 26 November 2021
Accepted 23 April 2022
Keywords
Anomaly detection
Factory automation
Autoencoder
Multivariate time series
JAALR2408
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