PanelTS: A Multi-Domain Benchmark Dataset for Panel-Based Time Series Forecasting

Many real-world forecasting problems involve multiple related units observed over time, each with its own target variable and associated covariates - a structure known as panel data. Yet existing time series benchmarks are predominantly organised as collections of independent univariate series or single-entity multivariate recordings, leaving panel-structured forecasting systematically underserved. We introduce PanelTS, a multi-domain benchmark dataset designed to close this gap. PanelTS adopts a unified representation that natively supports univariate, multivariate, and panel-based forecasting tasks while preserving cross-unit structure, enabling rigorous evaluation of models that exploit inter-unit dependencies. The benchmark comprises 1,844 datasets, spanning synthetic data with controllable temporal dynamics and real-world datasets drawn from COVID-19 epidemiology, exchange-traded funds (ETFs), currency markets, and equity markets, thereby providing diversity in domain, granularity, and scale. To facilitate reproducibility and systematic comparison, PanelTS provides standardised data formats, task definitions, and evaluation protocols across varying input lengths, prediction horizons, and temporal resolutions. We report baseline results across representative forecasting models to establish reference performance and demonstrate compatibility with existing approaches. PanelTS is publicly released to support the development and fair evaluation of the next generation of panel-aware forecasting methods: https://huggingface.co/datasets/Multiple-Time-Series/PanelTS (data), https://anonymous.4open.science/status/PanelTS-020D (code), https://huggingface.co/spaces/Echo0623/PanelTS-ICLR-Review (demo).