English(EN) MetaARIMA: Configuration-Level Transfer Learning for ARIMA Models

MetaARIMA将迁移学习引入ARIMA时间序列预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了MetaARIMA，这是一种将迁移学习应用于ARIMA模型配置以进行时间序列预测的新颖方法。与独立训练每个序列的传统方法不同，MetaARIMA从过去的实验中学习，为新数据集推荐有前景的ARIMA配置。该方法旨在通过利用不同预测任务之间的知识来提高效率和性能。实验表明，MetaARIMA的性能优于AutoARIMA，并且在数据效率方面与Chronos2和TimesFM等近期基础模型相比仍具竞争力。 AI

影响通过迁移学习增强了经典的统计预测方法，使其与现代基础模型更具竞争力。

排序理由该条目描述了一篇详细介绍时间序列预测新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Vitor Cerqueira, Ricardo In\'acio, Carlos Soares · 2026-10-27 04:00

MetaARIMA：用于ARIMA模型的配置级迁移学习

arXiv:2610.34171v1 Announce Type: new Abstract: Time Series Foundation Models have demonstrated that pretraining on diverse datasets enables effective transfer to new forecasting tasks. Classical methods like ARIMA, however, remain configured and trained independently for each se…

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MetaARIMA：用于ARIMA模型的配置级迁移学习

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