Scaling Multivariate Time Series Forecasting with Context-Modulated Prototype Attention

Multivariate time series forecasting is fundamental to a wide range of real-world applications, from traffic and energy systems to large-scale retail platforms, where hundreds to tens of thousands of interacting variables must be forecast jointly. Full pairwise attention captures rich cross-variate interactions but incurs quadratic complexity in the number of variates, while efficient alternatives reduce this cost by restricting or compressing cross-variate communication, potentially sacrificing flexible, input-dependent interactions. Real-world forecasting further requires incorporating known covariates, such as calendar events and promotions, and characterizing predictive uncertainty. We propose SLIM, a unified Transformer architecture built on context-modulated prototype attention (CoPA). CoPA compresses cross-variate interactions into a small set of latent prototypes formed from the history, and lets known covariates modulate only the queries that retrieve from them, so that the same shared representation is read differently under different forecast contexts. This yields cost linear in the number of variates, adds a covariate path that is independent of the prototype budget, and supports quantile forecasting within a single backbone. Extensive experiments across short-term, long-term, high-dimensional, and context forecasting benchmarks demonstrate state-of-the-art accuracy with substantially lower memory and computational costs.