通过全局平滑外生变量提升时间序列预测精度
Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
- 对外生变量做全局统计去冗余处理
- 在不增加历史窗口长度下提升长期依赖捕捉能力
- 适用于需要外生特征的时间序列场景
尽管多数基于Transformer的时间序列预测模型主要依赖内生输入，近期先进方法通过引入外生变量显著提升了性能。然而，这些方法存在两个问题：当内生与外生输入来自同一来源时易产生冗余；固定历史窗口限制了长期依赖的捕捉能力。本文提出一种对齐全局统计的外生变量去冗余方法，使外生信息更具备长期上下文感知能力。将此优化后的外生输入融入模型，无需延长历史窗口，即可有效引导模型提升预测表现。在四个基准数据集上，本方法均达到最先进水平，持续优于11个基线模型，验证了其在利用外生变量方面的鲁棒性与有效性。
原文摘要 · Abstract (English)
Although most transformer-based time series forecasting models primarily depend on endogenous inputs, recent state-of-the-art approaches have significantly improved performance by incorporating external information through exogenous inputs. However, these methods face challenges, such as redundancy when endogenous and exogenous inputs originate from the same source and limited ability to capture long-term dependencies due to fixed look-back windows. In this paper, we propose a method that whitens the exogenous input to reduce redundancy that may persist within the data based on global statistics. Additionally, our approach helps the exogenous input to be more aware of patterns and trends over extended periods. By introducing this refined, globally context-aware exogenous input to the endogenous input without increasing the lookback window length, our approach guides the model towards improved forecasting. Our approach achieves state-of-the-art performance in four benchmark datasets, consistently outperforming 11 baseline models. These results establish our method as a robust and effective alternative for using exogenous inputs in time series forecasting.
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