Country risk forecasting for major oil exporting countries: A decomposition hybrid approach

Authors:
Jian Ping Li;Ling Tang;Xiao Lei Sun;Le An Yu;Wan He;Yu Ying Yang
Affiliations:
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China and Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;MADIS, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China and Center for Forecasting Science, Chinese Academy of Sciences, ...;State Grid Energy Research Institute, Beijing 100052, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China and Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Venue:
Computers and Industrial Engineering
Year:
2012

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Abstract

From the perspective of energy security, this paper focuses on country risk forecasting for major oil exporting countries. Due to the two main characteristics of country risk of oil exporting countries, i.e. the complexity and the mutability, this study proposes a decomposition hybrid approach (DHA) for predicting country risk of oil exporters, based on the principle of ''decomposition and ensemble'' and the strategy of ''divide and conquer''. In DHA, effective decomposition methods, such as ensemble empirical mode decomposition (EEMD), are specially introduced to decompose oil exporter's country risk into a series of relatively easily forecasting components; powerful prediction tools, such as least squares support vector regression (LSSVR), are then implemented to predict all extracted components; and finally these predicted results are fused into an ensemble for the original data via ensemble approaches, such as LSSVR model or simple addition (ADD) approach. Experimental results, with ten major oil exporters as study samples, demonstrate that DHA with decomposition process can be statistically proved to be much stronger and more robust than other popular prediction models.