超越整体模型：深度多元时间序列预测的系统性组件级基准测试

摘要

先前关于多元时间序列预测的研究主要集中于开发复杂的整体模型，而本研究则主张转向对预测方法中关键组件的细粒度理解。我们提出TSCOMP，这是首个大规模基准测试，系统性地将深度预测方法解构为其核心的细粒度组件——涵盖序列预处理、编码策略、网络架构（包括专用及大规模时序模型）以及优化方法。通过约束正交实验设计与广泛评估，我们开展多视角分析，揭示了不同骨干网络、数据特征及其交互作用下的组件效能。除提供见解外，本基准建立了包含超过2万个模型-数据集评估的细粒度性能语料库，支持自动组件选择的学习，从而实现新数据集上的零样本模型构建。实验表明，尽管该语料库驱动方法简单，但其性能始终优于现有最先进方法，验证了评估设计的合理性，并确认系统化组件选择优于手动设计的复杂架构。所有代码与性能语料库均已在 https://github.com/SUFE-AILAB/TSCOMP 公开。

English

While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-level understanding of their impacts. We propose TSCOMP, the first large-scale benchmark that systematically deconstructs deep forecasting methods into their core, fine-grained components--spanning series preprocessing, encoding strategies, network architectures including specific and large time-series models, and optimization methods. Using constrained orthogonal experimental design and extensive evaluations, we conduct multi-view analyses that reveal component effectiveness across different backbones, data characteristics, and their interactions. Beyond providing insights, this benchmark establishes a fine-grained performance corpus comprising over 20,000 model-dataset evaluations, which supports the learning of automated component selection, enabling zero-shot model construction on new datasets. Our experiments demonstrate that the corpus-driven approach, despite its simplicity, consistently outperforms state-of-the-art methods, validating the soundness of our evaluation design and confirming that systematic component selection surpasses manually designed complex architectures. All code and the performance corpus are publicly available at https://github.com/SUFE-AILAB/TSCOMP.