Matryoshka: Evidence-Gated Nested Channel Mixing for Time Series Forecasting

In recent years, channel mixing has become a standard component of multivariate time series forecasting. Most existing methods either mix all channels or restrict mixing to a fixed or learned interaction structure, without testing whether the other channels carry predictive information. As a result, a module that helps on datasets with hundreds of channels can hurt on datasets with only a few. We propose **Matryoshka**, a plug-in module that wraps a forecasting backbone without modifying it. Inspired by the classical Granger causality test, Matryoshka formulates channel mixing as a selection problem, in which held-out evidence of predictive gain decides where and at what scale channels are mixed. The module arranges channels into nested neighborhoods built from a diffusion geometry of the training data, and keeps a neighborhood only if its shared history improves held-out prediction beyond each channel's own history. Each retained neighborhood receives a residual mixer, a “doll”, whose capacity grows with the neighborhood's size. The dolls start at zero, so training begins exactly at the backbone. Our theoretical analysis shows that the optimal neighborhood balances omitted cross-channel information against an estimation cost that grows linearly with its size. On datasets with hundreds of channels, Matryoshka reduces the MSE of its backbone by 22% on average on the unified benchmark and by 45% on the CI/CD benchmark.