Assessing the Performance of Transformer for Time Series Anomaly Detection

Abstract

This study aims to assess the effectiveness of the Transformer-based reconstruction approach for detecting anomalies in time series data. The reconstruction error-based anomaly detection method was applied to both multivariate time series from NASA SMAP/MSL and univariate time series from UCR. Four deep learning models, including Transformer, Dilated CNN, LSTM, and MLP, were compared in terms of their ability to reconstruct input data. Dilated CNN outperformed the other models in almost all experimental results, achieving a 25% higher score than Transformer on the UCR dataset when trained with random masking, and a 60% higher score when trained with middle masking. These results suggest that the Transformer did not perform as well as expected for anomaly detection based on time series reconstruction errors, and its inferiority to Dilated CNN may be attributed to the characteristics of the time series and the limited training data. Future research should focus on developing Transformer models that can better capture the properties of time series data and investigate the relationship between the model’s performance, data volume, and model complexity.