最近锚定与循环排序的时间序列预测

中文摘要 AI 辅助

长期预测模型通常使用相同的固定堆栈处理回看窗口中的所有补丁。因此，较旧的上下文补丁和最近的证据获得相同的计算深度。然而，最接近预测的信息与更遥远的上下文贡献并不相等。统一处理使得这种区别在架构中未被表达。我们提出了MARO，一种最近锚定与循环排序模型，该模型从最近的补丁到最旧的补丁处理回看窗口。最近的补丁作为锚点，初始化潜在状态并调节每个后续步骤，从而将较旧的补丁折叠到以最近证据为中心的表示中。一个共享模块在每一步被重用，因此将扫描进一步扩展到过去不会引入额外的参数。扫描期间保留的中间状态允许预测头分别权衡历史中的短期和长期部分。这通过循环细化的顺序表达了最近性。在多个真实世界时间序列数据集上的大量实验表明，MARO在长期和短期预测方面均达到了最先进的性能。消融研究检验了主要架构组件的贡献。

英文摘要

Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same computational depth. Yet the information closest to the forecast and the more distant context do not contribute equally. Uniform processing leaves this distinction unexpressed in the architecture. We propose MARO, a Most-Recent Anchoring with Recurrent Ordering model that processes the look-back window from the most recent patch to the oldest. The most recent patch serves as the anchor. It initializes the latent state and conditions each subsequent step, so older patches are folded into a representation that remains centered on recent evidence. A single shared module is reused at every step, so extending the scan further into the past introduces no additional parameters. Intermediate states retained during the scan allow the forecast head to weigh short and long portions of the history separately. This expresses recency through the order of recurrent refinement. Extensive experiments across multiple real-world time series datasets show that MARO achieves state-of-the-art performance on both long-term and short-term forecasting tasks.Ablation studies examine the contribution of the main architectural components.

发表机构

• University of Hildesheim（希尔德斯海姆大学）
• VWFS Data Analytics Research Center (VWFS-DARC)（VWFS数据分析研究中心）

机构由 AI 辅助整理，请以论文原文为准。