COIN: Coordinating Imputation and Adaptation for Financial Forecasting with Time-Series Foundation Models

Missing observations and changing statistical properties in financial time series affect the forecasting performance of pretrained time series foundation models. Selecting imputation methods based on reconstruction error does not guarantee better downstream forecasts. We propose COIN, a hierarchical framework that coordinates imputation and adaptation for rolling financial forecasting. On the data side, COIN feeds histories completed by multiple lightweight imputers separately into the backbone and dynamically weights their forecasts using prediction losses from completed windows. On the adaptation side, it selects a branch for each window from unadapted predictions, residual corrections, and LoRA outputs at different strengths. Both decision layers use only forecasting targets that have become available, without requiring ground-truth values at missing historical positions. The original backbone parameters remain frozen, and LoRA is trained once during initialization; rolling forecasting updates only weights, residuals, and scoring states. Experiments cover 11 financial data sources, four backbone models, and three initial missingness rates. On the development set, COIN reduces average mean absolute scaled error (MASE) by 2.87%–3.67% compared with forecasting pipelines using NuwaTS, MOMENT, Timer, and Moirai. In confirmation experiments on 22 additional series with fixed configurations, COIN achieves lower average error for all four backbones than an STL-Kalman pipeline with matched LoRA adaptation capabilities. Ablations and analyses under different conditions further show that gains vary across data sources, missingness locations, and feedback timeliness. These results support coordinating imputation candidates and output adaptation to improve existing foundation models for financial forecasting when subsequent target feedback is available.