S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries

arXiv:2404.16179v1 Announce Type: new
Abstract: Anomaly detection plays a crucial role in industrial settings, particularly in maintaining the reliability and optimal performance of cooling systems. Traditional anomaly detection methods often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness. And yet traditional anomaly detection often relies on application of single models. This work proposes a novel, robust approach using five heterogeneous independent models combined with a dual ensemble fusion of voting techniques. Diverse …

abstract anomaly anomaly detection arxiv challenges cooling cs.lg data detection detection methods diverse ensemble face framework fusion industrial noise performance prediction reliability role self-supervised learning supervised learning systems timeseries type voting