Affiliations: [a] School of Control Science and Engineering, Dalian University of Technology, Dalian, China
| [b] School of Economics, Dongbei University of Finance and Economics, Dalian, China
| [c] Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, Canada
| [d] Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| [e]
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Correspondence:
[*]
Corresponding author. Degang Wang, School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China. E-mail: [email protected].
Abstract: In this paper, a two stage forecasting process is proposed for interval-valued time series viz. time series whose values are intervals instead of numbers. The forecasting of interval-valued time series is realized through predicting the centers and the radii of the intervals. The proposed model consists of two functional modules: interval-valued threshold autoregression (ITAR) model followed by a granular fuzzy system. Fuzzy C-Means (FCM) method is used to determine the threshold parameters of the ITAR model while the least square error algorithm is used to estimate the values of its coefficients. To improve the forecasting accuracy, a granular fuzzy system is designed to further compensate for the series of residual errors. The proposed model can effectively capture the nonlinear feature of the original system. The piecewise compensation scheme can help to boost the prediction capability of the hybrid model. Some experiments demonstrate the performance of the model.
