Excellence in Research and Innovation for Humanity

International Science Index

Select areas to restrict search in scientific publication database:
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.
Digital Article Identifier (DAI):


[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.
Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007