用时间戳全局信息提升时间序列预测鲁棒性
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
- 将时间戳独立建模以捕捉全局依赖关系
- 在9个真实数据集上使主流模型平均性能提升12.5%
- 可作为插件适配任意预测模型，适合工业级时序任务
时间序列预测在金融、交通、能源、医疗和气候等领域具有关键作用。由于包含丰富的周期性信息，时间戳具备提供稳健全局引导的潜力。然而，现有方法多聚焦局部观测，将时间戳仅视为可选补充，未能充分挖掘其价值。当真实世界数据受污染时，缺乏全局信息会损害算法的鲁棒性。为此，本文提出一种新框架GLAFF：将时间戳独立建模以捕获全局依赖，并作为插件自适应调整全局与局部信息的融合权重，实现与任意时序预测主干模型的无缝协作。在九个真实数据集上的大量实验表明，GLAFF显著提升了主流预测模型的平均性能12.5%，超越此前最优方法5.5%。
原文摘要 · Abstract (English)
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal information they contain, timestamps possess the potential to offer robust global guidance for forecasting techniques. However, existing works primarily focus on local observations, with timestamps being treated merely as an optional supplement that remains underutilized. When data gathered from the real world is polluted, the absence of global information will damage the robust prediction capability of these algorithms. To address these problems, we propose a novel framework named GLAFF. Within this framework, the timestamps are modeled individually to capture the global dependencies. Working as a plugin, GLAFF adaptively adjusts the combined weights for global and local information, enabling seamless collaboration with any time series forecasting backbone. Extensive experiments conducted on nine real-world datasets demonstrate that GLAFF significantly enhances the average performance of widely used mainstream forecasting models by 12.5%, surpassing the previous state-of-the-art method by 5.5%.
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