提出统一多步预测策略的框架，提升预测性能并指导选择最佳方法。
Stratify: Unifying Multi-Step Forecasting Strategies
- 构建参数化框架Stratify，整合现有策略并生成新策略。
- 在18个数据集上测试，84%实验中新策略优于已有方法。
- 强调需根据任务特点选策略，适合需要精准预测的研究者。
时间序列领域的重要能力是进行多步预测（MSF），其核心在于选择合适的预测策略。然而，由于缺乏对策略空间的系统性梳理，实践者往往依赖经验选择。本文提出Stratify，一个参数化框架，统一现有策略并引入更优的新策略。我们在18个基准数据集、5类函数和10至80步的多种预测时长下评估该框架。在1080次实验中，超过84%的情况下，Stratify中的新策略表现优于所有已有策略。重要的是，没有一种策略在所有任务中始终最优，凸显了基于任务需求探索策略空间的重要性。本研究提供了迄今最全面的已知与新策略对比基准。代码已开源以支持复现。
原文摘要 · Abstract (English)
A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose Stratify, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate Stratify on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80). In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners explore the Stratify space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We make code available to reproduce our results.
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