Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application

데이터 전처리를 이용한 다중 모델 퍼지 예측기의 설계 및 응용

  • 방영근 (강원대 대학원 전기전자공학과) ;
  • 이철희 (강원대 전기전자공학부)
  • Published : 2009.01.01

Abstract

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

References

  1. George E. P. Box and Gwilym M. Jenkins, Time series analysis: Forecasting and Control, Holden-Day, 1970
  2. George J. Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic Theory and Applications, Prentice-Hall, 1995
  3. Juhong Nie, 'Nonlinear Time-Series Forecasting: A Fuzzy Neural Approach', Neuro computing, vol.16, pp.66-76, MacMaster University, 1997
  4. K.Ozawa, T.Niimura, 'Fuzzy Time-Series Model of Electric Power Consumption', IEEE Canadian conference on Electrical and Computer Engineering, pp.1195-1198, 1999 https://doi.org/10.1109/CCECE.1999.808235
  5. Daijin Kim, Chulhyun Kim, 'Forecasting Time Series with Genetic Fuzzy Predictor Ensemble'. IEEE Trans. on Fuzzy Systems, vol. 5, pp.523-535, 1997 https://doi.org/10.1109/91.649903
  6. O.Valenzuela, I.Rojas, F.Rojas, H.Pomares, L.J.Herrera, A.Guillen, L.Marquez. M.Pasadas, 'Hybridization of Intelligent techniques and ARIMA models for time series prediction', vol. 159, pp. 821-845, April 2008 https://doi.org/10.1016/j.fss.2007.11.003
  7. Inteak Kim, Song-Rock Lee, 'A Fuzzy Time Series Prediction Method based on Consecutive Values', IEEE International Fuzzy Systems, vol.2, pp.703-707, 1999 https://doi.org/10.1109/FUZZY.1999.793034
  8. Chul-Heui Lee, Sang-Hun Yoon, 'Fuzzy Nonlinear Time Series Forecasting with Data Preprocessing and Model Selection', Joural of Telecommunications and Information, vol.5, pp.232-238, 2001
  9. Carl G. Looney, 'Pattern Recognition using Neural Networks', Oxford University Press, 1997
  10. Stephen J. Redmond, Conor Heneghan, 'A method for initialising the K-means clustering algorithm using kd-trees', pattern recognition letters, vol. 28, pp. 965-973, 2007 https://doi.org/10.1016/j.patrec.2007.01.001
  11. 김해경, 김태수, 시계열 분석과 예측 이론, 문운당, 2003
  12. http://www-personal.buseco.monash.edu.au/
  13. 주용석, 유전알고리즘과, 러프집합을 이용한 퍼지 시스템 모델링, 강원대학교 석사학위논문, 2003
  14. L. X. Wang, J. M. Mendel, 'Generating fuzzy rules from numerical data, with applications', IEEE Trans. on Systems, Man, and Cybern, 22 No.6, pp1414-1427, 1992 https://doi.org/10.1109/21.199466
  15. 김인택, 공창욱, '시계열 예측을 위한 처지 학습 알고리즘', 한국 퍼지 지능시스템 학회, vol.7, No.3, pp. 34-42, 1997