Improved NN-GM(1,1) for Postgraduates’ Employment Confidence Index Forecasting

Lu Wang

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

Abstract:

Postgraduates’ employment confidence index (ECI) forecasting can help the university to predict the future trend of postgraduates’ employment. However, the common forecast method based on the grey model (GM) has unsatisfactory performance to a certain extent. In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1) model is combined with the improved neural network (NN) in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1) model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/465208.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/465208.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:465208

DOI: 10.1155/2014/465208

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().