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An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

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  • Li Liu
  • Qianru Wang
  • Ming Liu
  • Lian Li

Abstract

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 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 PRGM(1,1) gets much better forecast accuracy among other widely used grey models on three actual economic datasets.

Suggested Citation

  • Li Liu & Qianru Wang & Ming Liu & Lian Li, 2014. "An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:641514
    DOI: 10.1155/2014/641514
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    References listed on IDEAS

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    Cited by:

    1. Yanqiu Sun, 2014. "A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).

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