Improved Grey Theory-Based Model in Time Series Adaptive Prediction

Forecasting time series accurately is critical to ensure the safety and reliability of complex system. So, time series prediction has been a popular subject. Normally, the information used in time series prediction is always mined from multivariable time series and small simple data. Thus, based on grey prediction theory, an adaptive prediction model with multivariable small simple time series data is proposed. In this paper, after analyzing the disadvantages of GM(1,1) model, we modify the initial values and background values of GM(1,1) model, and then the interrelations and characteristics of the multiple variables time series are taken into account. In order to improve the prediction accuracy, we used particle swarm optimization (PSO) to obtain the optimal weight factor.. At last we proved that the model has good prediction precision by an experiment, which will be useful in applications.