DocumentCode :
3498828
Title :
Fuzzy Time Series Prediction with Data Preprocessing and Error Compensation Based on Correlation Analysis
Author :
Bang, Young-Keun ; Lee, Chul-Heui
Author_Institution :
Dept. of Electr. & Electron. Eng., Kangwon Nat. Univ., Chunchon
Volume :
2
fYear :
2008
fDate :
11-13 Nov. 2008
Firstpage :
714
Lastpage :
721
Abstract :
In general, it is difficult to predict non-stationary or chaotic time series since there exists drift and/or non-linearity as well as uncertainty in them. To overcome this situation, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. The proposed method uses the differences of time series as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and reveals better their implicit properties. In data preprocessing procedure, the candidates of optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated for them. And 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 best one which minimizes the performance index is selected, and it works on hereafter for prediction. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Computer simulation on some typical examples is performed to verify the effectiveness of the proposed method.
Keywords :
correlation theory; forecasting theory; fuzzy set theory; parameter estimation; pattern clustering; time series; chaotic time series; correlation analysis; data preprocessing; error compensation; fuzzy time series prediction; k-means clustering algorithm; least squares method; multiple model TS fuzzy predictors; multiple model bank; nonstationary time series; optimal difference interval; parameter identification; Chaos; Clustering algorithms; Data preprocessing; Error compensation; Least squares methods; Partitioning algorithms; Prediction methods; Predictive models; Time series analysis; Uncertainty; Fuzzy time series prediction; TS fuzzy predictor; correlation analysis; data preprocessing; error compensation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
Conference_Location :
Busan
Print_ISBN :
978-0-7695-3407-7
Type :
conf
DOI :
10.1109/ICCIT.2008.302
Filename :
4682329
Link To Document :
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