위협 탐지 및 교통량 예측을 위한 장기 시계열 데이터에서의 Transformer 와 LSTM 모델 성능 비교

세션 II: MR-IoT융합 인공지능 기술

위협 탐지 및 교통량 예측을 위한 장기 시계열 데이터에서의 Transformer 와 LSTM 모델 성능 비교
Comparison of Transformer and LSTM for threat detection and traffic prediction on long time-series data.

영어: Deep learning research to analyze industrial time-series data has been an active research topic. Recent studies have attempted to borrow the models for natural language processing(NLP) to handle time dependency issues. However, industrial data have different properties compared with NLP data: strongly dependent on a time axis. Moreover, because industrial information is continuously accumulating while the machine is running, it has a much longer sequence than other sequential data. In this study, we compare the performance of widely used natural language models, LSTM and Transformer, on such long-time series industrial data. For comparison, we performed experiments to detect an attack on a water treatment management system and to predict traffic flow on a highway. We confirmed that the Transformer using the attention mechanism showed better performance than the LSTM.

Seung Min Jang [ Department of Artificial Intelligence, Ajou University ]
Jeong-Hyeon Moon [ Department of Artificial Intelligence, Ajou University ]
Kyung-Ah Sohn [ Department of Artificial Intelligence, Ajou University ] Corresponding Author

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