An Improved EEMD-based Framework for CPI Forecasting

Author

Tao Xiong ; Zhongyi Hu ; Yukun Bao

Author_Institution

Dept. of Manage. Sci. & Inf. Syst., Huazhong Univ. of Sci.&Tech., Wuhan, China

fYear

2012

fDate

23-26 June 2012

Firstpage

22

Lastpage

26

Abstract

Although the Empirical Mode Decomposition (EMD)-based decomposition and ensemble framework for time series forecasting has been widely used, the end effect of EMD has not been addressed adequately. This study proposed to incorporate Mirror Method (MM), capable of dealing with the problem of end effect, into a hybrid modeling framework with Ensemble Empirical Mode Decomposition (EEMD) and Support Vector Machines (SVMs) for Consumer Price Index (CPI) Forecasting. The monthly Chinese CPI series from Jan. 2000 to Nov. 2011, with a total 143 observations, were employed to justify the performance of the proposed framework. The results suggested that it performed better than all the selected counterparts in terms of RMSE and SMAPE.

Keywords

pricing; support vector machines; time series; CPI forecasting; EEMD-based framework; MM; RMSE; SMAPE; SVM; consumer price index forecasting; empirical mode decomposition-based decomposition; end effect problem; ensemble framework; hybrid modeling framework; mirror method; support vector machines; time series forecasting; Forecasting; Kernel; Mirrors; Predictive models; Support vector machines; Training; CPI Forecasting; End Effect; Ensemble Empirical Mode Decomposition; Mirror Method; Time Series modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4673-1365-0

Type

conf

DOI

10.1109/CSO.2012.14

Filename

6274670