A noise robust and distribution-adaptive framework for multivariate time series anomaly detection

Existing unsupervised anomaly detection methods for multivariate time series(MTS) have demonstrated advanced performance on numerous public datasets. However, these methods exhibit two critical limitations: (1) the assumption of completely noise-free training data contradicts real-world conditions where normal samples are inevitably contaminated; (2) the inherent non-stationarity of MTS induces distribution shift, leading to biased learning and degraded generalization capabilities. This paper proposes NORDA, a novel MTS anomaly detection framework that integrates a multi-order difference mechanism with distribution shift optimization. Firstly, a multi-order difference mechanism performs multi-order explicit differencing on raw temporal signals, effectively mitigating noise interference during representation learning. Secondly, a mixed reversible normalization module is proposed, employing a normalization network with multiple statistical features to dynamically model non-stationary variations across variables. This module achieves remove and restore of non-stationary properties of MTS through a symmetric reversible architecture, thereby enhancing the dynamic adaptability of model to distribution shift. By synergistically integrating the two aforementioned modules with a Transformer-based multi-layer encoder, this framework can extract robust latent representations through modeling of inter-channel dependencies in differentially processed data streams. Extensive experiments on seven benchmark datasets demonstrate that NORDA significantly outperforms sixteen typical baseline methods while exhibiting great robustness against noise contamination.