A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts for time series monitoring.
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MICA adapts infini compressive attention to the channel dimension, enabling scalable cross-channel dependencies in Transformers and cutting forecast error by 5.4% on average versus channel-independent baselines.
Triplet fusion of 28 statistical features, 64-dim time-series embeddings from a 133K-param model, and 1024-dim text embeddings into LightGBM yields 0.992 precision and 0.998 AUC on 67k HVAC samples while cutting false positives by 83%.
citing papers explorer
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Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts for time series monitoring.
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MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
MICA adapts infini compressive attention to the channel dimension, enabling scalable cross-channel dependencies in Transformers and cutting forecast error by 5.4% on average versus channel-independent baselines.
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Triplet Feature Fusion for Equipment Anomaly Prediction : An Open-Source Methodology Using Small Foundation Models
Triplet fusion of 28 statistical features, 64-dim time-series embeddings from a 133K-param model, and 1024-dim text embeddings into LightGBM yields 0.992 precision and 0.998 AUC on 67k HVAC samples while cutting false positives by 83%.