解决多变量时间序列的依赖、异步采样和缺失值问题，提升实际场景预测稳定性。
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
- 基于Transformer设计，显式建模通道间交互关系。
- 在真实数据集上优于现有方法，处理异步与缺失数据更鲁棒。
- 适合工业级时序预测场景，尤其应对复杂现实约束。
现实世界的时间序列数据通常是多变量的，各通道间存在复杂的相互依赖关系。每个通道通常以不同的周期采样，且由于实际操作限制常出现缺失值。这些特性带来了通道依赖、采样异步性和缺失性三大挑战，需同时解决才能实现可靠的实际预测。然而，现有模型通常孤立处理部分问题，依赖简化假设，未能有效应对异步采样、测试时缺失块及复杂依赖的联合挑战。为此，我们提出ChannelTokenFormer，一种基于Transformer的统一框架，能显式捕捉跨通道交互，支持通道级异步采样，并有效处理缺失值。在多个公开基准数据集及一个私有工业真实数据集上的实验表明，该模型在复杂现实条件下展现出更优的鲁棒性和准确性。
原文摘要 · Abstract (English)
Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to missing values due to various practical and operational constraints. These characteristics pose three fundamental challenges involving channel dependency, sampling asynchrony, and missingness, all of which must be addressed simultaneously to enable robust and reliable forecasting in practical settings. However, existing architectures typically address only parts of these challenges in isolation and still rely on simplifying assumptions, leaving unresolved the combined challenges of asynchronous channel sampling, test-time missing blocks, and intricate inter-channel dependencies. To bridge this gap, we propose ChannelTokenFormer, a Transformer-based forecasting framework with a flexible architecture designed to explicitly capture cross-channel interactions, accommodate channel-wise asynchronous sampling, and effectively handle missing values. Extensive experiments on public benchmark datasets reflecting practical settings, along with one private real-world industrial dataset, demonstrate the superior robustness and accuracy of ChannelTokenFormer under challenging real-world conditions.
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