Why do forecasting models demand massive context windows?

Time series forecasting models use increasingly long input windows, but the reason has been unclear. This work shows models need to solve two problems simultaneously: identify which underlying process generated the data, then forecast future values. Long windows reduce uncertainty about the true process—a mathematical necessity even for patterns with short memory. Decoupling these tasks improves computational efficiency without sacrificing accuracy on synthetic and real datasets.