Work

Unsupervised Anomaly Detection in Time Series Data via Enhanced VAE-Transformer Framework

Year: 2025

Type: article

Abstract: Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poo... | Find, read and cite all the research you need on Tech Science Press

Source: Computers, materials & continua/Computers, materials & continua (Print)

DOI: 10.32604/cmc.2025.063151

Authors: Chunhao Zhang, Bin Xie, Zhibin Huo

Institutions: Chinese Academy of Geological Sciences, Hebei Normal University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, China Geological Survey

Awards: G2315438999, G3731106438