Efficiently controlling the early stages of a manufacturing system is an important issue for enterprises. However, the number of samples collected at this point is usually limited due to time and cost issues, making it difficult to understand the real situation in the production process. One of the ways to solve this problem is to use a small data set forecasting tool, such as the various gray approaches. The gray model is a popular forecasting technique for use with small data sets, and while it has been successfully adopted in various fields, it can still be further improved. This paper thus uses a box plot to analyze data features and proposes a new formula for the background values in the gray model to improve forecasting accuracy. The new forecasting model is called BGM(1,1). In the experimental study, one public dataset and one real case are used to confirm the effectiveness of the proposed model, and the experimental results show that it is an appropriate tool for small data set forecasting.
All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Applied Mathematics