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**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.

### MSC:

62P20 | Applications of statistics to economics |

62M20 | Inference from stochastic processes and prediction |

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\textit{L. Liu} et al., Abstr. Appl. Anal. 2014, Article ID 641514, 10 p. (2014; Zbl 1468.62426)

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### References:

[1] | Liu, X. Q.; Ang, B. W.; Goh, T. N., Forecasting of electricity consumption: a comparison between an econometric model and a neural network model, Proceedings of the 1991 IEEE International Joint Conference on Neural Networks (IJCNN ’91) |

[2] | Quah, T. S.; Srinivasan, B., Improving returns on stock investment through neural network selection, Expert Systems with Applications, 17, 4, 295-301 (1999) |

[3] | Rabiner, L. R., Tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 2, 257-286 (1989) |

[4] | Roman, J.; Jamee, A., Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns, Proceedings of the 29th Hawaii International Conference on System Sciences |

[5] | Tkacz, G., Neural network forecasting of Canadian GDP growth, International Journal of Forecasting, 17, 1, 57-69 (2001) |

[6] | de Gooijer, J. G.; Hyndman, R. J., 25 years of time series forecasting, International Journal of Forecasting, 22, 3, 443-473 (2006) |

[7] | He, W.; Wang, Z.; Jiang, H., Model optimizing and feature selecting for support vector regression in time series forecasting, Neurocomputing, 72, 1-3, 600-611 (2008) |

[8] | Shen, J.; Zhang, C.; Lian, C.; Hu, H.; Mammadov, M., Investment decision model via an improved BP neural network, Proceedings of the 2010 IEEE International Conference on Information and Automation (ICIA ’10) |

[9] | Kandel, A., Fuzzy Expert Systems (1992), CRC Press |

[10] | Ma, J.; Teng, J. F., Predict chaotic time-series using unscented kalman filter, Proceedings of 2004 International Conference on Machine Learning and Cybernetics |

[11] | Jo, T. C., The effect of virtual term generation on the neural based approaches to time series prediction, Proceedings of the 4th International Conference on Control and Automation (ICCA ’03) |

[12] | Lee, L. W.; Wang, L. H.; Chen, S. M., Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques, Expert Systems with Applications, 34, 1, 328-336 (2008) |

[13] | Chen, S. M.; Wang, N. Y.; Pan, J. S., Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships, Expert Systems with Applications, 36, 8, 11070-11076 (2009) |

[14] | Wong, H. L.; Tu, Y. H.; Wang, C. C., Application of fuzzy time series models for forecasting the amount of Taiwan export, Expert Systems with Applications, 37, 2, 1465-1470 (2010) |

[15] | Kayacan, E.; Ulutas, B.; Kaynak, O., Grey system theory-based models in time series prediction, Expert Systems with Applications, 37, 2, 1784-1789 (2010) |

[16] | Deng, J., Grey Prediction and Decisionmaking (1989), Wuhan, China: Huazhong University of Science and Technology Press, Wuhan, China |

[17] | Hsu, C. C.; Chen, C. Y., Applications of improved grey prediction model for power demand forecasting, Energy Conversion and Management, 44, 14, 2241-2249 (2003) |

[18] | Hsu, L. C., A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry, Expert Systems with Applications, 37, 6, 4318-4323 (2010) |

[19] | Hsu, L. C., Using improved grey forecasting models to forecast the output of opto-electronics industry, Expert Systems with Applications, 38, 11, 13879-13885 (2011) |

[20] | Ju-Long, D., Control problem of grey systems, Systems & Control Letters, 1, 5, 288-294 (1982) · Zbl 0482.93003 |

[21] | Akay, D.; Atak, M., Grey prediction with rolling mechanism for electricity demand forecasting of Turkey, Energy, 32, 9, 1670-1675 (2007) |

[22] | Tang, H. W. V.; Yin, M. S., Forecasting performance of grey prediction for education expenditure and school enrollment, Economics of Education Review, 31, 4, 452-462 (2012) |

[23] | Zhao, Z.; Wang, J.; Zhao, J.; Su, Z., Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China, Omega, 40, 5, 525-532 (2012) |

[24] | Wang, J.; Dong, Y.; Wu, J.; Mu, R.; Jiang, H., Coal production forecast and low carbon policies in China, Energy Policy, 39, 10, 5970-5979 (2011) |

[25] | Wang, Z. X.; Hipel, K. W.; Wang, Q.; He, S. W., An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China, Applied Mathematical Modelling, 35, 12, 5524-5532 (2011) |

[26] | Chang, S. C.; Lai, H. C.; Yu, H. C., A variable \(P\) value rolling Grey forecasting model for Taiwan semiconductor industry production, Technological Forecasting and Social Change, 72, 5, 623-640 (2005) |

[27] | Eberhart, R.; Kennedy, J., A new optimizer using particle swarm theory, Proceedings of the 6th International Symposium on Micromechatronics and Human Science |

[28] | Poli, R., Analysis of the publications on the applications of particle swarm optimisation, Journal of Artificial Evolution and Applications, 2008 (2008) |

[29] | Tan, G., The structure method and application of background value in grey system gm(1,1) model (i), Systems Engineering: Theory and Practice, 4, 4, 98-103 (2000) |

[30] | Hu, X.; Shi, Y.; Eberhart, R., Recent advances in particle swarm, Proceedings of the 2004 Congress on Evolutionary Computation (CEC ’04) |

[31] | Shi, Y.; Eberhart, R., Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation (CEC ’99) |

[32] | Kennedy, J.; Eberhart, R. C.; Shi, Y., Swarm Intelligence (2001), San Francisco, Calif, USA: Morgan Kaufmann Publishers, San Francisco, Calif, USA |

[33] | van den Bergh, F., An analysis of particle swarm optimizers [Ph.D. thesis] (2002), University of Pretoria |

[34] | Trelea, I. C., The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, 85, 6, 317-325 (2003) · Zbl 1156.90463 |

[35] | Shi, Y.; Eberhart, R. C., Parameter selection in particle swarm optimization, Evolutionary Programming VII. Evolutionary Programming VII, Lecture Notes in Computer Science, 1447, 591-600 (1998) |

[36] | Yokuma, J. T.; Armstrong, J. S., Beyond accuracy: comparison of criteria used to select forecasting methods, International Journal of Forecasting, 11, 4, 591-597 (1995) |

[37] | Hsu, L. C.; Wang, C. H., Forecasting the output of integrated circuit industry using a grey model improved by the Bayesian analysis, Technological Forecasting and Social Change, 74, 6, 843-853 (2007) |

[38] | Chen, C. F.; Lai, M. C.; Yeh, C. C., Forecasting tourism demand based on empirical mode decomposition and neural network, Knowledge-Based Systems, 26, 281-287 (2012) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.