An intelligence optimized rolling grey forecasting model fitting to small economic dataset. (English) Zbl 1468.62426

Summary: Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular, and nonstationary. The size of these economic datasets is often very small. Many models based on grey system theory could be adapted to various economic time series data. However, some of these models did not consider the impact of recent data or the effective model parameters that can improve forecast accuracy. In this paper, we proposed the \(\text{PRGM}(1,1)\) model, a rolling mechanism based grey model optimized by the particle swarm optimization, in order to improve the forecast accuracy. The experiment shows that \(\text{PRGM}(1,1)\) gets much better forecast accuracy among other widely used grey models on three actual economic datasets.


62P20 Applications of statistics to economics
62M20 Inference from stochastic processes and prediction
Full Text: DOI


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