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<p>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 consistency, and weak adaptability to downstream tasks. To address these challenges, we propose a novel conditional generative adversarial framework that integrates statistical feature augmentation, multi-scale temporal windows, and a composite loss function combining adversarial, L2, DTW, FID, and statistical constraints. This design ensures that the generated data preserves both local dynamics and global distributional properties. Our method introduces a systematic strategy for loss calibration and architecture tuning, which enhances generation stability without the need for complex temporal signature modeling. Experimental results on three real-world industrial datasets demonstrate that our model achieves superior generation fidelity and efficiency compared to state-of-the-art baselines. Specifically, our method achieves the lowest DTW score and the lowest MMD value, surpassing MTSS-GAN and TCGAN by 17.3% and 21.7%, respectively. In terms of training cost, our model achieves a training time reduction of 11.4% compared to MTSS-GAN. These results validate the effectiveness and efficiency of our proposed framework in real-world time-series generation scenarios.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Kai Lang,
  • Yonghua Li

摘要

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 consistency, and weak adaptability to downstream tasks. To address these challenges, we propose a novel conditional generative adversarial framework that integrates statistical feature augmentation, multi-scale temporal windows, and a composite loss function combining adversarial, L2, DTW, FID, and statistical constraints. This design ensures that the generated data preserves both local dynamics and global distributional properties. Our method introduces a systematic strategy for loss calibration and architecture tuning, which enhances generation stability without the need for complex temporal signature modeling. Experimental results on three real-world industrial datasets demonstrate that our model achieves superior generation fidelity and efficiency compared to state-of-the-art baselines. Specifically, our method achieves the lowest DTW score and the lowest MMD value, surpassing MTSS-GAN and TCGAN by 17.3% and 21.7%, respectively. In terms of training cost, our model achieves a training time reduction of 11.4% compared to MTSS-GAN. These results validate the effectiveness and efficiency of our proposed framework in real-world time-series generation scenarios.