Task-aware conditional GAN with multi-objective loss for realistic and efficient industrial time series generation

Task-aware conditional GAN with multi-objective loss for realistic and efficient industrial time series generation

Abstract Industrial time-series data generation is critical for addressing data scarcity, improving model robustness, and enabling data-driven decision-making in complex manufacturing systems. However, existing generative models often suffer from poor temporal fidelity, limited statistical consisten...

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Bibliographic Details
Main Authors: Kai Lang, Yonghua Li
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Journal of Big Data
Subjects:
Conditional generative adversarial networks (CGAN)
Time series synthesis
Knowledge information
Dynamic time warping (DTW)
Fréchet inception distance (FID)
Online Access:https://doi.org/10.1186/s40537-025-01266-8
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https://doi.org/10.1186/s40537-025-01266-8

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