Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm

Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting.

Research highlights
► Grey model is used for small sample forecasting.
► Chaotic particle swarm optimization algorithm optimizes the parameters of grey model.
► Propose the concept of optimal input subset for CPSO-based grey model.
► Use the new approach for China financial revenue forecasting.