超越整體模型：深度多變量時間序列預測的系統性組件層級基準測試

摘要

先前在多变量时间序列预测领域的研究主要聚焦于开发复杂的整体模型，而本研究则倡导转向对组件影响的颗粒化解构式理解。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拆解为其核心细粒度组件——涵盖序列预处理、编码策略、网络架构（包括专用型与大规模时间序列模型）以及优化方法。通过采用受限正交实验设计与广泛评估，我们开展了多视角分析，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。除提供洞见外，该基准还建立了包含超过20,000项模型-数据集评估的细粒度性能语料库，为自动化组件选择的学习提供支持，从而实现在新数据集上的零样本模型构建。实验表明，尽管方法本身简洁，这种基于语料库的驱动方式始终优于现有最新方法，验证了我们评估设计的合理性，并确认系统化的组件选择胜过人工设计的复杂架构。所有代码与性能语料库均已公开于 https://github.com/SUFE-AILAB/TSCOMP。

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.