Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.