PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
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.
Code (1)
Tasks
Irregular Time SeriesMissing ValuesTime SeriesTime Series ClassificationSimilar Papers 제목 키워드 기반
Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for large-scale automatic discovery in the dynamic universe, but existing tim…
Representation LearningDomain AdaptationIIT-GAN: Irregular and Intermittent Time-series Synthesis with Generative Adversarial Networks
Time-series data is one of the most popular data types in the field of machine learning. For various reasons, there is a strong motivation to synthesize fake time-series data. Several disparate settings for time-series s…
Missing ValuesTime SeriesTime Series AnalysisUnleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to …
Time SeriesTime Series AnalysisZero-Shot LearningTiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit divers…
Multivariate Time Series ForecastingIrregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregula…
Irregular Time SeriesTime SeriesTime Series Analysis