English(EN) Forecasting Voltage Time Series under Anomalies Using a WGAN Model With Lipschitz Control and Progressive Kernel Critic Strategy

新的WGAN模型通过异常检测增强电压时间序列预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种用于电压时间序列的新型预测模型，该模型集成了异常检测功能。该模型基于具有自适应核的Wasserstein GAN (WGAN-CLP)，旨在处理高维数据并改进长期动态建模。WGAN-CLP框架能够动态地将其核适应新的数据窗口，从而增强其预测未来值和重建时间序列流的能力。实验结果表明，该方法在电压和能源时间序列预测的准确性和鲁棒性方面均优于基准方法。 AI

影响引入了一种新颖的深度学习框架，用于改进能源相关数据中的时间序列预测和异常检测。

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arXiv cs.LG TIER_1 English(EN) · Najmieh Sadat Safarabadi · 2026-10-19 04:00

使用具有Lipschitz控制和渐进核批评策略的WGAN模型预测异常电压时间序列

arXiv:2610.23394v1 Announce Type: new Abstract: In time-series forecasting tasks, the use of recursive forecasting on high-dimensional data substantially increases the complexity of the prediction process. The main challenge is that long-term dynamics becomes harder to model, sin…

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使用具有Lipschitz控制和渐进核批评策略的WGAN模型预测异常电压时间序列

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