Time-Series Forecasting Models Care Less About What Humans See

Time-series forecasting predicts future values from historical observations to support energy scheduling, weather prediction, and traffic management. In recent years, Fourier operations have been widely used in time-series forecasting models. Their reported gains are widely attributed to the explicit representation of periodic structure with a prescribed Fourier basis. Fourier operations commonly introduce multiple filtered views of the same history, but existing studies have not analyzed whether the reported gains come from the Fourier basis or the accompanying view expansion. We examine this attribution with matched Random controls. We replace Fourier filters or bases with norm- and parameter-matched Random counterparts. We conduct 96 matched comparisons across generic backbones, backbone scales, and eight published models, covering input histories with varying degrees of periodic repetition. Fourier shows no aggregate forecasting advantage over Random, with 47 Fourier wins, 47 Random wins, and two ties. Both representations often improve over Raw. Further analysis shows that their shared gain primarily reflects expanding each history into multiple distinct filtered views. Increasing the response count usually improves forecasting, whereas longer filters provide no corresponding benefit. Prescribed frequency structure can provide additional gains when it matches repeated patterns within the input window.