Learning Latent Actions from Joint Histories for Multivariate Time-Series Forecasting

In multivariate time series forecasting, the same target history can warrant different futures as surrounding observations change. Forecast accuracy alone leaves the context-induced revision unlocalized. ACT defines a forecasting state–action interface: it retains a representation encoded from the target history alone, uses joint histories to select a replaceable action, and reads the updated state with a shared forecast head. A fixed-state intervention protocol replaces only that action, making its forecast effect directly measurable and exactly replayable. LAB is one realization of this interface, using structured feature and variable interactions for exact action extraction and low-rank implementation; direct scaling provides a second realization under the same interface. Across 20 models on four datasets, actions selected from the observed joint history beat zero, equal-magnitude noise, and shuffled actions; direction and target matching matter beyond magnitude. Across five Solar sites and 2,527 active site–time pairs, the observed context lowers six-hour mean squared forecast error by 28.0% relative to substitutions matched on target history and daily phase; replay reconstructs the forecast difference to numerical precision. ACT is competitive with 12 forecasters across eight benchmarks. Relative to dense LAB, low-rank LAB cuts training-and-validation time per epoch by about 42% on Electricity and 56% on Traffic.