Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection

Multivariate time series anomalies often manifest as shifts in
cross-channel dependencies rather than simple amplitude excursions. In
autonomous driving, for instance, a steering command might be internally
consistent but decouple from the resulting lateral acceleration. Residual-
based detectors can miss such anomalies when flexible sequence models
still reconstruct signals plausibly despite altered coordination.
We introduce AxonAD, an unsupervised detector that treats multi-
head attention query evolution as a short horizon predictable process. A
gradient-updated reconstruction pathway is coupled with a history-only
predictor that forecasts future query vectors from past context. This is
trained via a masked predictor-target objective against an exponential
moving average (EMA) target encoder. At inference, reconstruction error
is combined with a tail-aggregated query mismatch score, which mea-
sures cosine deviation between predicted and target queries on recent
timesteps. This dual approach provides sensitivity to structural depen-
dency shifts while retaining amplitude-level detection. On proprietary
in-vehicle telemetry with interval annotations and on the TSB-AD multi-
variate suite (17 datasets, 180 series) with threshold-free and range-aware
metrics, AxonAD improves ranking quality and temporal localization over
strong baselines. Ablations confirm that query prediction and combined
scoring are the primary drivers of the observed gains. Code is available
at the URL https://github.com/iis-esslingen/AxonAD.