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.

翻译：不规则时间序列数据具有记录频率多变、观测时长不一以及数值缺失等特点，在移动计算、医疗健康和环境科学等领域带来了显著的分析挑战。现有研究社群往往忽视这些挑战，或仅针对局部问题进行孤立处理，导致工具与方法呈现碎片化。为弥合这一差距，我们提出了一个统一框架，并构建了首个基于通用数组格式、旨在提升互操作性的不规则时间序列分类标准化数据集库。该库整合了34个数据集，并在此之上对来自不同领域与研究社群的12种分类器模型进行了系统性基准测试。本研究旨在整合相关研究方向，为不规则时序数据分析方法提供更稳健的评估基础。