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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：NEURAL NETWORK WORLD

收录刊物：SCIE、EI、SSCI、Scopus

卷号：26

期号：3

页面范围：219-232

ISSN号：1210-0552

关键字：BSI prediction; Support Vector Machine (SVM); multi-step; hybrid

摘要：Accurate prediction of the Baltic index makes great difference to the strategic decision and risk avoidance of the enterprise. For the multi-step Baltic Supermax Index prediction, direct prediction and iterative prediction has its own advantages. Therefore, in this paper, in combination with direct and iterative prediction, based on Support Vector Machine (SVM), a hybrid multistep prediction model is put forward. In hybrid model, the output from the iterative model is a rough prediction and it need also be adjusted based on the output from the direct model. And weekly BSI data from January 2011 to November 2014 are used to test the model. The results show that the hybrid multistep prediction model based on SVM has high accuracy, and is feasible in the BSI prediction.