关键词: 改进粒子群算法, 组合预测, 权重, 物流需求预测

Abstract: In order to improve the accuracy of combined forecasting, it is necessary to study the weights allocation of forecasting model. The paper uses Particle Swarm Optimization （PSO） to solve the weight of combination forecast model. In the process of research, aiming at the shortcomings of the basic particle swarm algorithm, inertia weight and acceleration factor of the parameters of particle swarm optimization are improved. And the combination forecast model based on improved particle swarm algorithm is constructed. To Chongqing logistics demand forecasting as the background, with four methods as reference object, it compares to verify the effectiveness of the improved model and the accuracy of prediction.

Key words: improved particle swarm optimization, combined forecasting, weight, logistics demand forecasting