Journal
ABSTRACT AND APPLIED ANALYSIS
Volume -, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2014/641514
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Funding
- Cuiying Grant of China Telecom, Gansu Branch [lzudxcy-2013-3]
- Science and Technology Planning Project of Chengguan District, Lanzhou [2013-3-1]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [44th]
- Fundamental Research Funds for the Central Universities [XDJK2014C141, SWU114005]
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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.
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