Forecasting practice from Box-Cox transformation models

In this paper, forecasting analysis to Box-Cox transformation models with a practical example is considered. Based on chosen generalized functional form, variables influencing passenger are selected by statistic mechanism, not just by subjective judgment or dependent on certain specified model, and forecasting models are constructed. Comparing with typical linear regression forecasting models, nonlinear forecasting models are more effective and precise. Based on collecting data and final forecasting models, forecasting results are obtained and forecasting errors are analyzed. Finally, some helpful conclusions can be drawn from this study.