Abstract: Aimed at the normal transformation of input data in the Bayesian processor of forecasts (BPF), the impacts of the meta-Gaussian model (MG) and Box-Cox transformation (BC) on the performance of a BPF model are compared and discussed. We make the normal transformation of the BPF model’s input data using MG and BC separately, construct a BPF-MG model and a BPF-BC model for probability forecasting, and analyze the forecasting capabilities of these two models in the conditions of different forecast periods and different data sizes. Results indicate that when the number of data samples is small, BPF-MG can achieve a higher stability through its complicated MG procedure, but it involves more complicated transformation than the BPF-BC model that has a very sensitive transform coefficient. With an increasing data size, the BPF-BC model is improved in forecasting capability and its BC coefficient becomes more stable.