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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, renderi
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