Impute or Forecast Directly? A Systematic Benchmark for Time Series Forecasting with Missing Values

Time series forecasting with missing values (TSFMV) is commonly addressed through either impute-then-forecast pipelines or end-to-end missingness-aware models. Recent studies increasingly advocate end-to-end modeling to avoid error propagation between separately optimized imputation and forecasting stages. Whether this design consistently improves forecasting performance, however, remains unclear because existing studies differ substantially in their datasets, missingness settings, and evaluation protocols. We introduce TSFMV-Bench, a systematic benchmark spanning three real-world datasets, seven missingness scenarios, and three missing rates. Beyond performance rankings, we investigate when the relative advantage between the two paradigms changes and how different missingness characteristics shape forecasting difficulty. Our evaluation yields three findings. First, strong impute-then-forecast pipelines remain competitive across a majority of evaluated conditions, challenging the common concern that stage-wise error propagation necessarily makes them inferior. Second, neither paradigm dominates uniformly, and their relative advantage changes with the type and severity of missingness. Third, paradigm preference is shaped by both the recoverability of missing observations and where reconstruction errors occur. Cross-variable information helps only when supporting observations remain available, while errors near the forecasting boundary can substantially disadvantage two-stage pipelines. These findings challenge a blanket preference for end-to-end modeling and motivate evaluating both paradigms across diverse missing-data settings. Code and benchmark resources are available in [anonymous repository](https://anonymous.4open.science/r/TSFMV-Bench-BE81).