Application of RBF-STARMA Model in Shipping Flow Forecasting

Abstract

Based on the idea of the neural network, intelligent computing methods are used to analyze temporal and spatial data. We present the temporal and spatial autocorrelation moving average (STARMA) model based on the in-depth systematic study on time sequence of hybrid model. Firstly this paper uses radial basis function neural network to extract the temporal and spatial sequence which is non-stationary caused by large-scale non-linear trend, secondly this paper presents STARMA modeling of small-scale random spatial and temporal variation. Comparative analysis between the original data and the forecasting data shows that proposed hybrid model has better performance of fitting and generalization.