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.

中文翻译：

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一种考虑数据间隙补偿的灰色预测新模型

目的

本文的目的是开发一种小数据集预测方法，以提高管理决策的有效性。

设计/方法/方法

在灰色建模过程中，合适的背景值是决定预测精度的关键因素之一。本文开发了灰色补偿项，使背景值更合适，进一步提高灰色模型的预测精度。

发现

在实验中，通过三个真实案例来验证所提出方法的有效性。实验结果表明，该方法可以提高灰度预测的准确率。结果进一步表明，由提出的补偿项确定的背景值可以提高三种情况下灰色模型的准确性。

原创性/价值

以往的研究在传统灰色建模过程的限制内确定了合适的背景值，而本研究则不受限制地创造了新的背景值。实验结果将鼓励研究人员在确定背景值时不受限制地开发更准确的灰度模型。