This talk investigates how external uncertainty estimates and expert specialization can enhance neural time-series forecasting. Across two complementary studies, we examine the mechanisms underlying these improvements, how design choices shape them, and the conditions that limit their effectiveness.
The first study asks whether quantile forecasts from an external predictor can guide a diffusion-based probabilistic forecaster. We investigate how different forms of quantile integration shape forecasting performance and find that residual conditioning throughout the network yields the most consistent improvements on the primary backbone. The resulting method, Control-TS, employs a trainable controller that injects quantile-derived corrections into a frozen diffusion forecaster, improving its probabilistic accuracy across all eight evaluated benchmarks. We also introduce the Conditional Linear Predictive Score (CLPS), which assesses the downstream forecasting utility of generated futures using a linear model.
The second study disentangles expert specialization from model capacity and routing in multivariate forecasting. Controlled comparisons show that informative assignments of observed channels improve forecasting beyond matched shared capacity and random allocation, while the benefit of input-dependent routing varies across backbones. Synthetic experiments reveal how persistent channel information supports specialization when short observation windows conceal predictive differences. Motivated by these findings, we develop a plug-and-play framework that augments a frozen pretrained forecaster with low-rank residual experts. Alternating expert learning with closed-form estimation of channel-specific mixture weights tailors these corrections to individual channels while preserving the original predictor.