首个统一框架与数据集，专为不规则时间序列分类而设计
PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
- 构建统一数组格式，兼容多种不规则时间序列数据
- 涵盖34个数据集，评测12种分类模型性能表现
- 适合医疗、环境等领域的时序数据分析研究者使用
不规则时间序列具有变化的采样频率、不同的观测时长和缺失值，广泛存在于交通、医疗与环境科学等领域。现有研究常孤立处理这些问题，导致工具与方法分散。本文提出首个统一框架及标准化数据集仓库，基于通用数组格式提升互操作性。该仓库包含34个数据集，并在其中对12种来自不同领域和社区的分类器模型进行基准测试。本工作旨在集中研究力量，推动不规则时间序列分析方法的更稳健评估。
原文摘要 · Abstract (English)
Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods.
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