# 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

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-26 08:00
- AIHOT 分数：54
- AIHOT 链接：https://aihot.virxact.com/items/cmpv2s2j8034wsl0z0n63z1ai
- 原文链接：https://arxiv.org/abs/2605.26562

## AI 摘要

本文提出了TSCOMP，首个将深度预测方法系统分解为序列预处理、编码策略、网络架构和优化方法等细粒度组件的大规模基准。通过约束正交实验设计和广泛评估，分析了组件在不同骨干网络和数据特性下的有效性。该基准构建了包含超过20000次模型-数据集评估的细粒度性能语料库，支持自动化的组件选择，能够实现零样本模型构建。实验表明，基于该语料库的方法始终优于当前最优方法。

## 正文

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.