PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
This work addresses the fragmented tools and methods in irregular time series analysis for fields like mobility and healthcare, though it is incremental as it focuses on standardization rather than new algorithmic breakthroughs.
The authors tackled the problem of irregular time series analysis by introducing a unified framework and standardized dataset repository, resulting in benchmarks of 12 classifier models across 34 datasets to centralize research efforts.
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.