We invite researchers and practitioners working with nonstationary, high-noise time series in industrial, physiological, or socio-technical systems to explore joint methodological development.
Focus areas include adaptive modeling, interpretable anomaly detection, and real-world deployment of pattern recognition frameworks.
Approaches based on machine learning, numerical analysis, signal decomposition, or hybrid modeling are all welcome.
Contact: song.qq@scifs.ac.cn
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