Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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A Pre-processing Process Using TadGAN-based Time-series Anomaly Detection

TadGAN 기반 시계열 이상 탐지를 활용한 전처리 프로세스 연구

  • Lee, Seung Hoon (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Kim, Yong Soo (Department of Industrial System Engineering, Kyonggi University)
  • 이승훈 (경기대학교 일반대학원 산업경영공학과) ;
  • 김용수 (경기대학교 산업시스템공학과)
  • Received : 2022.07.13
  • Accepted : 2022.08.05
  • Published : 2022.09.30
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Abstract

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

Keywords

Acknowledgement

본 연구는 경기도의 경기도 지역협력연구센터 사업의 일환으로 수행하였음[GRRC경기2020-B03, 산업통계 및 데이터마이닝 연구].

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