Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine

Title:

Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine

Creator:

Guan , F., Peng, Z., Wang, K., Song, X., and Gao, J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:86520586-09b4-4828-8569-3e6595415e64
uuid:86520586-09b4-4828-8569-3e6595415e64
doi:10.14311/NNW.2016.26.012

Subject:

BSI prediction, Support Vector Machine (SVM), multi-step, and hybrid

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:3

Harvested from:

CDK

Metadata only:

false