DiverseFlow: Jointly Modeling Forecasting Diversity and Uncertainty for Time Series Forecasting

Time-series forecasting is fundamental to many real-world applications, however, temporal observations often exhibit non-stationary dynamics and inherent uncertainty. Existing end-to-end forecasters typically learn a single deterministic mapping from historical observations to future values, which can limit forecast diversity and fails to characterize multiple plausible future outcomes. We propose DiverseFlow, it is a unified framework that jointly models forecast diversity and predictive uncertainty. DiverseFlow employs multiple structurally aligned predictors to capture complementary predictive views and introduces conditional residual flow matching to model flexible predictive distributions around deterministic forecasts. To stabilize distribution learning across heterogeneous temporal dynamics, we further develop conditional scale normalization, which decouples residual magnitude from distributional shape and allows the flow model to learn normalized residual dynamics more effectively. Crucially, DiverseFlow exploits complementary information from flow-generated forecasts to diversify deterministic predictors, enabling uncertainty modeling to directly improve point forecasting rather than serving only as an auxiliary probabilistic objective. Extensive experiments on ten real-world benchmark datasets show that DiverseFlow consistently improves point-forecasting accuracy over competitive baselines and achieves superior probabilistic forecasting performance over conditional Gaussian modeling across multiple metrics. Ablation studies further verify the effectiveness of forecast diversification, conditional scale normalization, and residual flow matching.