基于时间变量感知学习的分布式滞后Transformer用于可解释的多变量时间序列预测
Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting
AI总结：
提出DLFormer架构，通过集成分布式滞后嵌入和时间变量感知学习机制，在十个数据集上实现了高精度且可解释的多变量时间序列预测。
摘要：
时间序列数据是大数据分析的关键要素，常见于金融、医疗、气候预测和交通等领域。在大规模真实世界环境中，此类数据通常是高维且多变量的，需要既准确又可解释的高级预测方法。尽管基于Transformer的模型在多变量时间序列预测（MTSF）中表现良好，但其缺乏可解释性，限制了在关键应用中的使用。为克服这一问题，我们提出了Distributed Lag Transformer（DLFormer），一种用于可解释且可扩展MTSF的新型Transformer架构。DLFormer集成了分布式滞后嵌入和时间变量感知学习（TVAL）机制，以结构化地建模局部和全局时间依赖性，并显式捕捉过去变量对未来结果的影响。在十个基准和真实世界数据集上的实验表明，DLFormer实现了最先进的预测精度，同时提供了对变量维度和时间动态的稳健、可解释的见解。这些结果突显了DLFormer弥合性能与可解释性之间差距的能力，使其非常适合实际的大数据预测任务。
英文摘要：
Time series data is a key element of big data analytics, commonly found in domains such as finance, healthcare, climate forecasting, and transportation. In large scale real world settings, such data is often high dimensional and multivariate, requiring advanced forecasting methods that are both accurate and interpretable. Although Transformer based models perform well in multivariate time series forecasting (MTSF), their lack of explainability limits their use in critical applications. To overcome this, we propose Distributed Lag Transformer (DLFormer), a novel Transformer architecture for explainable and scalable MTSF. DLFormer integrates a distributed lag embedding and a time variable aware learning (TVAL) mechanism to structurally model both local and global temporal dependencies and explicitly capture the influence of past variables on future outcomes. Experiments on ten benchmark and real world datasets show that DLFormer achieves state of the art predictive accuracy while offering robust, interpretable insights into variable wise and temporal dynamics. These results highlight ability of DLFormer to bridge the gap between performance and explainability, making it highly suitable for practical big data forecasting tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
