Title: Beyond Extrapolation: Knowledge Utilization with Bidirectional Inference for Time Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Time-series forecasting, Bidirectional inference, Structural future prior

Abstract: Time-series forecasting has recently attracted considerable attention due to its crucial role in domains such as energy demand prediction, traffic flow management, and public health.  However, most existing forecasting models rely solely on unidirectional inference from history to future.  While effective in stable scenarios, this paradigm lacks explicit structural constraints about the future, which makes it challenging to stabilize predictions under complex dynamics. In this work, we take a different path: we propose a knowledge utilization paradigm with bidirectional inference, which introduces a future prior stream alongside the current input stream to impose active constraints during prediction. Based on the natural “history (model input)–target (ground-truth output)–future continuation” chain in time series, we present a reference framework that leverages candidate ratios learned from the history→future mappings of training chains whose histories match the current input to estimate an auxiliary future-prior stream, whose feature representation is obtained separately from that of the current input; the two feature representations are then fused at the feature level by a lightweight gating network. This design transforms forecasting from a purely extrapolative task into an interpolation-like process, enabling the model not only to rely on static knowledge stored in its parameters but also to dynamically reference learned future priors as compensation, thereby reducing uncertainty in volatile scenarios. 
Across six benchmarks and several state-of-the-art models, our paradigm consistently improves forecasting with small overhead.