A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection

While existing reconstruction-based multivariate time series (MTS) anomaly detection methods demonstrate advanced performance on many challenging real-world datasets, they generally assume the data only consists of normal samples when training models. However, real-world MTS data may contain significant noise and even be contaminated by anomalies. As a result, most existing approaches easily capture the pattern of the contaminated data, making identifying anomalies more difficult. Although a few studies have aimed to mitigate the interference of the noise and anomalies by introducing various regularizations, they still employ the objective of fully reconstructing the input data, impeding the model from learning an accurate profile of the MTS's normal pattern. Moreover, it is difficult for existing methods to apply the most appropriate normalization schemes for each dataset in various complex scenarios, particularly for mixed-feature MTS. This paper proposes a filter-augmented auto-encoder with learnable normalization (NormFAAE) for robust MTS anomaly detection. Firstly, NormFAAE designs a deep hybrid normalization module. It is trained with the backbone end-to-end in the current training task to perform the optimal normalization scheme. Meanwhile, it integrates two learnable normalization sub-modules to deal with the mixed-feature MTS effectively. Secondly, NormFAAE proposes a filter-augmented auto-encoder with a dual-phase task. It separates the noise and anomalies from the input data by a deep filter module, which facilitates the model to only reconstruct the normal data, achieving a more robust latent representation of MTS. Experimental results demonstrate that NormFAAE outperforms 17 typical baselines on five real-world industrial datasets from diverse fields.