Forecast, Guide, Refine: Future State Guidance for Time Series Forecasting

Future target representations can complement observation space supervision in multivariate time series forecasting. However, we identify Predicted State Deviation: a future state predicted from encoded history can inaccurately represent the true future, making it unreliable for direct decoding into a final forecast. Yet such an imperfect prediction may still provide useful guidance. We propose FUSE, a future state guidance framework following a forecast, guide, and refine paradigm. FUSE first generates a base forecast in observation space and encodes it as a base state, while predicting a future state from encoded history. Rather than directly decoding the predicted state, FUSE learns how far to move the base state along or against the displacement toward it. The resulting refined state is decoded to observation space and combined with an adjusted reconstruction residual to preserve base forecast information. FUSE can also be directly applied to existing backbones without retraining or modifying the backbone. Experiments on eight benchmarks show competitive accuracy with a simple MLP and consistent average improvements across three backbones. Ablations validate state refinement and residual adjustment, while decomposition analyses reveal corrections in forecast mean, waveform scale, and residual shape.