Науковий вісник НГУ

Self-adaptive optimized market prediction model based on grey model

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Economy and management

Last Updated on 02 October 2016

Published on 02 October 2016

Hits: 7480

Authors:

Jinchuan Wang, School of Marxism, Hubei Polytechnic University, Huangshi, Hubei, China

Abstract:

Purpose. Market prediction refers to prediction of internal rules and future development trends of various market indexes and factors based on exploration and in-depth research of various factors influencing market demand and supply changes through scientific theories and systematic model algorithms. This paper analyses optimization results of the traditional prediction algorithms and the intelligent prediction algorithms.

Methodology. In order to analyse differences between the results obtained by prediction algorithms and practical situations, it is necessary to unify the model analysis parameters. The model predicting the grey system is applicable to predicting situations with an index variation trend. The time sequence model is suitable to data with certain trend and periodical changes.

Findings. It has been found that the neural network and model and the support vector model have no requirements of data, so they are suitable to any situations. And when the market demand changes show index changes, the dynamic pricing and inventory control optimization model based on the grey prediction model is of vital guiding significance towards planning of commodity sales.

Originality. Through simulation, the predicted value of models is close to the final optimization target earnings.

Practical value. The following fact has been also considered: when market demand changes tend to show index changes, the dynamic pricing and inventory control optimization model based on the grey prediction model can guide the planning of commodity sales.

References/Список літератури

1. Li, W, Yuan, Y.N. and Dong, W.D., 2011. Study on engineering cost forecasting of electric power construction based on time response function optimization grey model. Proc. of IEEE 3rd International Conference on Communication Software and Networks, pp. 58–61.

2. Wu, R.Z., He, Y. and Tang, L.R., 2013. Traffic prediction based on grey model optimized by buffer operator and PSO in communication network for electric power. Applied Mechanics & Materials, Vol. 397–400, pp. 1994–1998.

3. Chen Youjun, He Hongying and Wei Yong, 2014. Optimization of grey prediction model using nonlinear programming method. Computer Engineering and Applications, Vol. 50, No. 10, pp. 61–64.

4. Dai, L.X. and Hu, F.F., 2012. Application optimization of grey model in power load forecasting. Advanced Materials Research, Vol. 347–353, pp. 301– 305.

5. Li, G.D., Masuda, S. and Nagai, M., 2013. The prediction model for electrical power system using an improved hybrid optimization model. International Journal of Electrical Power & Energy Systems, Vol. 44, No. 1, pp. 981–987.

6. Zu, X.H., Wang, J. and Yang, C.L., 2014. The GUI-Based simulation optimization platform for fault grey prediction of diesel engine. Applied Mechanics & Materials, Vol. 687–691, pp. 1049–1053.

7. Xiong, P.P, Dang, Y.G. and Yao, T., 2012. The research on the modelling method of background value optimization in grey Verhulst Model. Chinese Journal of Management Science, Vol. 20, No. 6, pp. 154– 159.

8. Wei, L. I., Yuan, Y. N. and Niu, D. X., 2011. Long and medium term load forecasting based on grey model optimized by buffer operator and time response function. Power System Protection & Control, Vol. 39, No. 10, pp. 59–63.

9. Zhao, J. and Chen, L. A., 2011. Application of an improved grey algorithm for short-circuit current online prediction in the low-voltage distribution system. Proc. of 2011 IEEE International Conference on Grey Systems and Intelligent Services, pp. 410– 413.

10. Xiong, P.P., Dang, Y.G. and Wu, X.H., 2011. Combined model based on optimized multi-variable grey model and multiple linear regression. Journal of Systems Engineering and Electronics, Vol. 22, No. 4, pp. 615–620.

04_2016_Jinchuan
2016-09-26  483.09 KB  684

• < Prev
• Next >