English(EN) Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty

Chronos-2 和 TimesNet 在不确定性下的负荷预测基准测试

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一篇新论文在不同程度的协变量不确定性下，对短期负荷预测（STLF）的时间序列基础模型（TSFM）进行了基准测试。研究发现，当未来协变量可用或预测准确时，Chronos-2 的表现最佳；而在协变量预测存在噪声时，TimesNet 显示出更强的鲁棒性。该研究强调了可靠的协变量建模对于有效的 STLF 应用的重要性。 AI

影响强调了时间序列基础模型在协变量不确定性下的性能差异，为电力系统中的实际应用提供了信息。

排序理由该集群包含一篇介绍机器学习模型基准测试的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Tomas Kaljevic, Ivan Arzola, Yu Zhang · 2026-10-07 04:00

协变量不确定性下用于负荷预测的时间序列基础模型的基准测试

arXiv:2610.07232v1 Announce Type: new Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation models (TSFMs) have recently demonstrated remarkable performance across a wide r…

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协变量不确定性下用于负荷预测的时间序列基础模型的基准测试

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