Abstract
The purpose of this paper is to discuss the state of fluctuation for the transportation demand which varies with time and to develop a time series prediction system and model considering properties of the fluctuation. The traditional models in time series analysis of data have problems such as insufficient consideration on the trend variation, complexity with including the seasonal variation and ambiguous process of model fitting.A general prediction system and a new stochastic prediction model in time series analysis, AROP model, is proposed. Prediction system consists of subsystems to separate stochastic stationary or nonstationary process, to select a prediction model and to evaluate the precision of prediction. AROP model is divided into two models, AROP 1 and AROP 2, corresponding to trend variation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
