Evidence-Guided Action World Model for Time-Series Forecasting

Time-series forecasting aims to predict future system states from historical observations and supports decision-making in applications such as energy management and retail planning. While modern forecasting methods effectively model endogenous temporal dynamics, real-world systems are also influenced by external factors that can alter their future evolution. Existing approaches typically incorporate such information as additional predictive inputs, directly learning mappings from external evidence to future observations. However, this treatment leaves the underlying dynamic response implicit. Importantly, the same external evidence may induce different responses depending on the current system state, making it difficult for existing methods to fully exploit external evidence. We therefore argue that forecasting should explicitly model the dynamic response implied by external evidence and characterize how this response changes system dynamics. Motivated by this perspective, we formulate forecasting under external evidence as an action-conditioned world-model problem, where latent actions explicitly represent dynamic responses to external influences and modulate future state transitions. We propose the Evidence-Guided Action World Model (EGA-WM), which learns a latent action space through a primitive bank, infers state-dependent actions from external evidence, and incorporates them into latent dynamics while modeling their persistent effects. Experiments on electricity-market and retail datasets demonstrate the effectiveness of EGA-WM across diverse forecasting scenarios.