Exploiting variable correlation with masked modeling for anomaly detection in time series.
Published in Spotlight Presentation at RobustSeq @ NeurIPS 2022., 2022
Recommended citation: Lymperopoulos, P., Li, Y., & Liu, L. (2022). Exploiting variable correlation with masked modeling for anomaly detection in time series. https://openreview.net/pdf?id=TCJuzs585W
Online anomaly detection in time series is a challenging task, especially when the time-series are stochastic. We propose a novel approach to exploit the correlation between variables in time series data to improve the performance of anomaly detection.
Recommended citation: Lymperopoulos, P., Li, Y., & Liu, L. (2022). Exploiting variable correlation with masked modeling for anomaly detection in time series.