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

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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Bibliographic Details
Main Authors: Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Univariate time-series
anomaly detection
siamese neural networks
1D CNNs
quantile smoothing
predictive modeling
Online Access:https://ieeexplore.ieee.org/document/10966923/
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