arXiv:2610.18904v1 Announce Type: cross Abstract: A reading departs from what a monitoring system expects for one of three reasons: the physical process changed, the instrument is failing, or the val...
arXiv:2606. 02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels.
arXiv:2606. 13486v1 Announce Type: cross Abstract: Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types -- point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) -- each requiring different feature representations.
arXiv:2610.35568v1 Announce Type: cross Abstract: We introduce ExCal, a normal-only multivariate time series anomaly detector that combines raw state retrieval, a signal/derivative subspace, and robu...
arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.
arXiv:2608. 15351v1 Announce Type: new Abstract: Nominal LoRA rank is a design parameter; calibrated spectral evidence is a separate inferential quantity.
arXiv:2608. 07899v1 Announce Type: new Abstract: Agent systems increasingly expose execution traces, yet telemetry that reveals a failure may still be inadequate for identifying where that failure originated.
arXiv:2606. 08935v1 Announce Type: cross Abstract: Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks.
arXiv:2607. 16811v3 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.
arXiv:2604. 17388v3 Announce Type: replace-cross Abstract: Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables.