A Comparison of Grey Model and Fuzzy Predictive Model for Time Series

The prediction of meteorological parameters at a meteorological station is an interesting and open problem. A firstorder linear dynamic model GM(1,1) is the main component of the grey system theory. The grey model requires only a few previous data points in order to make a real-time forecast. In this paper, we consider the daily average ambient temperature as a time series and the grey model GM(1,1) applied to local prediction (short-term prediction) of the temperature. In the same case study we use a fuzzy predictive model for global prediction. We conclude the paper with a comparison between local and global prediction schemes.

Authors:

• A. I. Dounis
• P. Tiropanis
• D. Tseles
• G. Nikolaou
• G. P. Syrcos

Keywords:

• Fuzzy predictive model
• grey model
• local andglobal prediction
• meteorological forecasting
• time series.

References:

[1] A. F. Atiya, S. M. El-Shoura, S. I. Shaheen and M. S. El-Sherif, A
comparison between neural-network forecasting techniques-case study:
river flow forecasting, IEEE Tran. On Neural Networks, Vol. 10, No. 2,
March 1999, pp. 402-409.
[2] T-C Chang, K-L Wen, H-T Chang, M-L You, Inverse approach to find
an optimum ╬▒ for grey prediction model, 1999 IEEE International
Conference on SMC, Oct. 1999, Tokyo Japan, pp. III (309- 313), 12-15.
[3] A. I. Dounis, D. L. Tseles, D. Belis, M. Daratsianakis, Neuro-Fuzzy
Network for Ambient Temperature Prediction, Neties- 97 European
Conference of Networking Entities, Ancona, 1-3 Oct. 1997.
[4] A. I. Dounis, B. Brachos, B. Stathouliss, D. L. Tseles, Neuro-Fuzzy
Logic System for Mean Daily Solar Radiation, 2nd Conference
Automation and Technology, Thessalonica 2-3 October, 1998, pp. 194-
199.
[5] A. I. Dounis, G. Nikolaou, D. Piromalis and D. L. Tseles, Model free
predictors for meteorological parameters forecasting: a review, 1st
International Scientific Conference on Information Technology and
Quality, Athens 5-6 June, 2004, A.2.3.
[6] Y.-P. Huang and C.-C. Huang, The integration and application of fuzzy
and grey modeling methods, Fuzzy Sets and Systems 78, 1996, pp. 107-
119.
[7] Y.-P. Huang and C.-H. Huang, Real-valued genetic algorithms for fuzzy
grey prediction system, Fuzzy Sets and Systems 87, 1997, pp. 265-276.
[8] Y.-P. Huang and Tai-Min Yu, The hybrid grey-based models for
temperature prediction, IEEE SMC-B, Vol. 27, No. 2, April 1997, pp.
284-292.
[9] J.-S. R. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing,
Prentice Hall, 1996.
[10] C.-F. Juang and C.-T. Lin, An Οn-Line Self-Constructing Neural Fuzzy
Inference Network and Its Application, IEEE on FS Vol. 6, Feb. 1998,
pp. 12-32.
[11] D. Kim and C. Kim, Forecasting Time Series with Genetic Fuzzy
Predictor Ensemble, IEEE Tr. on Fuzzy Systems, Vol. 5, No. 4, Nov.
1997, pp. 523-535.
[12] A. Mountis, G. Levermore, Weather prediction for feedforward control
working on the summer data, 5th Conference on Technology and
Automation, Thessalonici, Greece, 15-16 October, 2005.
[13] B. Reinaldo, Silveira, Shigetoshi and Sugahara, NN for local
meteorological forecasting, 3rd Conference on Artificial Intelligence
Applications to the Environmental Science, AMS, Feb. 2003 D.
[14] Riordan, B. K. Hansen, A fuzzy case-based system for weather
prediction, Engineering Intelligent Systems, Vol. 10, no. 3, 2000, pp.
139-146.
[15] S.-F. Su, C.-B. Lin, Y.-T. Hsu, A high precision global prediction
approach based on local prediction approaches, IEEE SMC-C, Vol.23,
No.4, Nov. 2002, pp. 416-425.
[16] L. X. Wang, Adaptive Fuzzy Systems and Control Design and Stability
Analysis, PTR Prentice-Hall, 1994.
[17] L. X. Wang, The WM Method Completed: A Flexible Fuzzy System
Approach to Data Mining, IEEE on FS, Vol. 11, No. 6, Dec. 2003, pp.
768-782.