With the widespread availability of sensor data across industrial and
operational systems, we frequently encounter heterogeneous time series from
multiple systems. Anomaly detection is crucial for such systems to facilitate
predictive maintenance. Cependant, most existing anomaly detection methods are
designed for either univariate or single-system multivariate data, making them
insufficient for these complex scenarios. To address this, we introduce
M$^2$AD, a framework for unsupervised anomaly detection in multivariate time
series data from multiple systems. M$^2$AD employs deep models to capture
expected behavior under normal conditions, using the residuals as indicators of
potential anomalies. These residuals are then aggregated into a global anomaly
score through a Gaussian Mixture Model and Gamma calibration. We theoretically
demonstrate that this framework can effectively address heterogeneity and
dependencies across sensors and systems. Empirically, M$^2$AD outperforms
existing methods in extensive evaluations by 21% on average, and its
effectiveness is demonstrated on a large-scale real-world case study on 130
assets in Amazon Fulfillment Centers. Our code and results are available at
https://github.com/sarahmish/M2AD.

Cet article explore les excursions dans le temps et leurs implications.

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