BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting
Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing evaluations are often conducted on a limited set of datasets with inconsistent experimental protocols and predominantly error-based metrics, rendering it difficult to compare and assess methods fairly and comprehensively across diverse settings. To eliminate these limitations and accelerate progress, we propose BITS, a standardized, reproducible, and extensible benchmark for advancing research on irregular time series forecasting. BITS covers eleven datasets from nine domains with diverse irregularity characteristics, and it characterizes ...
| Authors | Kangjia Yan, Linfeng Wang, Tianen Shen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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