Latent Propagation for Continual-Context Time-Series Forecasting

Time series forecasting is a fundamental task in machine learning, aiming to predict future horizons from historical observations. Prior methods have largely been developed under a *fixed-length formulation*, where a continuous time series is divided into input-output pairs of fixed-length input and prediction windows. This formulation, however, treats each forecasting instance independently, relying only on the observations within the fixed-length input window without maintaining context beyond it. As a result, it cannot leverage useful information accumulated from past observations over time. In this paper, we consider a *continual-context formulation*, where historical context can be accumulated over time beyond the fixed-length input window. Based on this formulation, we propose **CoLP**, which constructs **Co**ntext-carrying **L**atents for each instance and **P**ropagates them along the instance stream, enabling context to accumulate over time. Specifically, our framework treats queries corresponding to future horizons as latent contexts that aggregate prediction-relevant historical information and carry it forward to later instances. Through extensive experiments, we empirically demonstrate the superiority of our framework over baselines across various benchmarks, highlighting the potential of the continual-context formulation.