CONFIGURING BEFORE PREDICTION: RETHINKING TIME-SERIES FORECASTING TASK FORMULATION

Time-series forecasting research has devoted substantial effort to improving how models predict, while largely taking for granted what historical input they should predict from. Yet the historical input is itself a key part of the forecasting task formulation: it determines both what temporal structure is exposed to the predictor and how far into the past that structure extends. In common forecasting practice, these aspects are typically predetermined rather than adapted to the time series and forecast horizon at hand. We therefore propose a framework that jointly configures the temporal structure and temporal extent of the historical input for each channel and forecast horizon before prediction, selecting among reversible temporal representations and candidate historical extents. To make heterogeneous input formulations comparable without repeatedly fitting downstream models, we establish a unified risk characterization of their expected multi-step forecasting behavior on the original data scale, enabling principled configuration directly from the training series. Experiments on public benchmarks show that the resulting configurations consistently improve forecasting accuracy over commonly used fixed configurations across diverse forecasting architectures, including DLinear, PatchTST, and Koopman-based models. The improvements also persist across different choices of linear operators, demonstrating that the benefits are not tied to a particular predictor. Notably, even a simple scalar autoregressive forecaster becomes competitive with substantially more complex forecasting models when equipped with the configured historical input. These results highlight historical input formulation as an important yet underexplored dimension of time-series forecasting and show that configuring what a model predicts from can be as consequential as improving how it predicts.