Scholarlink

<p><?tk 2?>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 <i>Stratify</i>, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate <i>Stratify</i> on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80) in the univariate setting. In over 84% of 1080 experiments, novel strategies in <Emphasis Type="BoldItalic">Stratify</Emphasis> 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 to explore the <i>Stratify</i> 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 share the code (<a href="https://github.com/zs18656/stratify_unifying_MSF">https://github.com/zs18656/stratify_unifying_MSF</a>) to reproduce our results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stratify: unifying multi-step forecasting strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月6日

查看全文
  • Riku Green,
  • Grant Stevens,
  • Zahraa S. Abdallah,
  • Telmo M. Silva Filho

摘要

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 the univariate setting. 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 to 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 share the code (https://github.com/zs18656/stratify_unifying_MSF) to reproduce our results.