A Data-Driven Fuzzy Information Granulation Approach for Freight Volume Forecasting

The performance of the logistic system is one of the most important aspects in regional economy, and the freight volume is the biggest part of the logistic system. In this paper, an information granulation method is introduced to represent the freight volume in a fuzzy manner. After the characteristic features have been extracted from the raw time-series data and represented as information granules, the granules are modeled with the support vector machine (SVM). In consideration of both algorithm efficiency and prediction accuracy, an efficient version of SVM called least square (LS) SVM is employed and integrated with a parameter optimization algorithm, the particle swarm optimization. Simulation results on a real dataset illustrate the performance of the proposed method, and comparison studies are carried out with LS and partial LS-based methods.