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%.
Dam inflow time series regres- sion models minimising loss of hydropower op- portunities
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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%.