EpiCastBench：多变量流行病预测的数据集与基准测试
EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting
AI总结：
本文提出EpiCastBench，提供40个多变量流行病数据集，用于评估15种多变量预测模型，涵盖统计基线到最新深度学习方法，提升流行病预测的鲁棒性与公平性。
摘要：
随着数据驱动决策在公共卫生中的普及，流行病预测已成为关键研究领域。近期多变量预测模型在捕捉复杂时间依赖性方面优于传统单变量方法。然而，缺乏高质量基准测试集限制了稳健流行病预测方法的发展。为此，我们提出了EpiCastBench，一个大规模基准框架，包含40个精心编纂的多变量流行病数据集。这些公开数据集涵盖多种传染病和地理区域，具有不同的时间粒度、序列长度和稀疏性特征。我们分析这些数据集以识别其全局特征和结构模式。为确保可重复性和公平比较，我们建立了标准化评估设置，包括统一的预测时间范围、一致的预处理流程、多样的性能指标和统计显著性检验。通过利用该框架，我们对15种多变量预测模型进行了全面评估，涵盖统计基线到最先进的深度学习和基础模型。所有数据集和代码均在Kaggle和GitHub上公开。
英文摘要：
The increasing adoption of data-driven decision-making in public health has established epidemic forecasting as a critical area of research. Recent advances in multivariate forecasting models better capture complex temporal dependencies than conventional univariate approaches, which model individual series independently. Despite this potential, the development of robust epidemic forecasting methods is constrained by the lack of high-quality benchmarks comprising diverse multivariate datasets across infectious diseases and geographical regions. To address this gap, we present EpiCastBench, a large-scale benchmarking framework featuring 40 curated (correlated) multivariate epidemic datasets. These publicly available datasets span a wide range of infectious diseases and exhibit diverse characteristics in terms of temporal granularity, series length, and sparsity. We analyze these datasets to identify their global features and structural patterns. To ensure reproducibility and fair comparison, we establish standardized evaluation settings, including a unified forecasting horizon, consistent preprocessing pipelines, diverse performance metrics, and statistical significance testing. By leveraging this framework, we conduct a comprehensive evaluation of 15 multivariate forecasting models spanning statistical baselines to state-of-the-art deep learning and foundation models. All datasets and code are publicly available on Kaggle (https://www.kaggle.com/datasets/aimltsf/epicastbench) and GitHub (https://github.com/aimltsf/EpiCastBench).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
