PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks

Francesco Spinnato, Cristiano Landi

Keywords: irregular time series, classification

TL;DR: This work introduces a unified framework and the first standardized repository for irregular time series classification, enabling consistent evaluation of 12 classifiers across 34 datasets to address fragmented approaches to irregular temporal data.

Abstract: 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.

Dataset URL: https://huggingface.co/datasets/splandi/alembics-bowls-flasks

Code URL: https://github.com/fspinna/pyrregular

Primary Area: Evaluation (e.g., data collection methodology, data processing methodology, data analysis methodology, meta studies on data sources, extracting signals from data, replicability of data collection and data analysis and validity of metrics, validity of data collection experiments, human-in-the-loop for data collection, human-in-the-loop for data evaluation)

Submission Number: 324