A new grey prediction model considering the data gap compensation

Abstract

Purpose

The purpose of this paper is to develop a small data set forecasting method to improve the effectiveness when making managerial decisions.

Design/methodology/approach

In the grey modeling process, appropriate background values are one of the key factors in determining forecasting accuracy. In this paper, grey compensation terms are developed to make more appropriate background values to further improve the forecasting accuracy of grey models.

Findings

In the experiment, three real cases were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can improve the accuracy of grey predictions. The results further indicate that background values determined by the proposed compensation terms can improve the accuracy of grey model in the three cases.

Originality/value

Previous studies determine appropriate background values within the limitation of traditional grey modeling process, while this study makes new background values without the limitation. The experimental results would encourage researchers to develop more accuracy grey models without the limitation when determining background values.

Citation

Chang, C.-J., Chen, C.-C., Dai, W.-L. and Li, G. (2021), "A new grey prediction model considering the data gap compensation", Grey Systems: Theory and Application, Vol. 11 No. 4, pp. 650-663. https://doi.org/10.1108/GS-07-2020-0087