Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks

In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-sta...

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Detalhes bibliográficos

Principais autores:	Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Univariate time-series anomaly detection siamese neural networks 1D CNNs quantile smoothing predictive modeling
Acesso em linha:	https://ieeexplore.ieee.org/document/10966923/
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