The purpose of this work is to compare the local fitting and global fitting approaches while applying regression model to the PTT-BP data for the prediction of exercise blood pressures. We used linear and nonlinear regression models to represent the PTT-BP relationship during exercise. PTT-BP data were acquired both under resting state and also after cycling exercise with several load conditions. PTT was calculated as the time between R-peak of ECG and the peak of differential photo-plethysmogram. For the identification of the regression models, we used local fitting which used only the resting state data and global fitting which used the whole region of data including exercise BP. The results showed that the global fitting was superior to the local fitting in terms of the coefficient of determination and the RMS (root mean square) error between the experimental and estimated BP. The nonlinear regression model which used global fitting showed slightly better performance than the linear one (no significant difference). We confirmed that the wide-range of data is required for the regression model to appropriately predict the exercise BP.