A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities

• Small-data-set forecasting problem is difficult for most manufacturing environments.
• Short-term predictions using new limited data for engineers are more effective and efficient.
• The proposed method can analyze data features to improve forecasting performance.
• The ANGM(1,1) is considered an proper procedure to forecast with small samples.

In the early stages of manufacturing systems, it is often difficult to obtain sufficient data to make accurate forecasts. Grey system theory is one of the approaches to deal with this issue, as it uses fairly small sets to construct forecasting models. Among published grey models, the current non-equigap grey models can deal with data having unequal gaps, and have been applied in various fields. However, these models usually use fixed modeling procedures that do not consider data growth trend differences. This paper utilizes the trend and potency tracking method to determine the parameter α of the background value to build an adaptive non-equigap grey model to improve forecasting performance. The experimental results indicate that the proposed method considers that data occurrence properties can obtain better forecasting results.