Learning from Zeros: Regime Aware Sparse Forecasting

Sparse time series, where a large fraction of observations are exactly zero, are common in retail and supply chain forecasting. Conventional methods for this multi-horizon panel setting address only one facet of the challenge at a time, such as smoothing zeros away, decomposing occurrence and magnitude, or routing series by a fixed sparsity threshold. We argue that the temporal arrangement of zeros carries information about an underlying state and should be modeled as a continuous latent regime shared across the forecast horizon. We show theoretically that marginal objectives cannot identify how sparse events are jointly organized across a forecast window, that a shared latent regime can induce this dependence, and that the independent per-horizon regimes underlying common architectures cannot express it regardless of training. We propose a regime-aware framework built on this insight, combining a sparsity-aware encoder with a context-conditioned latent regime to produce a joint predictive distribution over Negative Binomial outputs. Across a large-scale proprietary e-commerce dataset and three public benchmarks, our method achieves consistent improvements over existing approaches, with joint-level calibration metrics confirming our theoretical predictions.