Traditional distance-based metrics like MSE and RMSE increasingly fail to differentiate between optimized forecasting models, making ensemble weighting difficult. This piece proposes using spectral entropy from information theory as an alternative metric for ensemble model weighting in time-series forecasting. Applied to inflation data (CPI, PPI, savings rate, business inventories), the author demonstrates that entropy-based inference can separate model performance where distance metrics cannot. A rough entropy inference ensemble scheme is outlined and compared against a distance-based ensemble, with results showing comparable accuracy but room for improvement. The approach is framed as nascent and open to extension, with entropy optimization suggested as a natural next step.
