A Fuzzy ARIMA Correction Model for Transport Volume Forecast

Abstract

In recent years, few water transportation forecasting studies conduct relative to transportation forecasting. As a neglected area, the inland waterway volume prediction is an important indicator for investment management and government policymaking. Considering the time-series forecasting, some researchers try to narrow the predicted value interval. However, certain limitations detract from their popularity. For instance, if the prediction length is more than ten, the result would not be acceptable. Therefore, we propose a hybrid model that combines both of their unique properties' advantages to provide more accurate traffic volume forecasts. Also, the forecasting process will be more straightforward. The empirical results present the proposed model and improve the long-term predictive accuracy at waterway traffic volume.