HEURISTIC BIVARIATE FORECASTING MODEL OF MULTI-ATTRIBUTE FUZZY TIME SERIES BASED ON FUZZY CLUSTERING

Guofang Nan (), Shuaiyin Zhou (), Jisong Kou () and Minqiang Li ()
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Guofang Nan: College of Management and Economics, Tianjin University, Weijin Road 92, Nankai District, Tianjin 300072, China
Shuaiyin Zhou: College of Management and Economics, Tianjin University, Weijin Road 92, Nankai District, Tianjin 300072, China
Jisong Kou: College of Management and Economics, Tianjin University, Weijin Road 92, Nankai District, Tianjin 300072, China
Minqiang Li: College of Management and Economics, Tianjin University, Weijin Road 92, Nankai District, Tianjin 300072, China

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 01, 167-195

Abstract: Fuzzy time series has been applied to forecast various domain problems because of its capability to deal with vagueness and incompleteness inherent in data. However, most existing fuzzy time series models cannot cope with multi-attribute time series and remain too subjective in the partition of the universe of discourse. Moreover, these models do not consider the trend factor and the corresponding external time series, which are highly relevant to target series. In the current paper, a heuristic bivariate model is proposed to improve forecasting accuracy, and the proposed model applies fuzzy c-means clustering algorithm to process multi-attribute fuzzy time series and to partition the universe of discourse. Meanwhile, the trend predictors are extracted in the training phase and utilized to select the order of fuzzy relations in the testing phase. Finally, the proper full use of the external series to assist forecasting is discussed. The performance of the proposed model is tested using actual time series including the enrollments at the University of Alabama, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a sensor dataset. The experimental results show that the proposed model can be utilized for multi-attribute time series and significantly improves the average MAER to 1.19% when compared with other forecasting models.

Keywords: Fuzzy time series; bivariate model; fuzzy clustering; forecasting; time-invariant series (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622012500083

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