Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 265, Issue -, Pages 400-408Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2015.05.006
Keywords
Forecasting; Small data set; Gray model
Categories
Funding
- K.C. Wong Magna Fund in Ningbo University
Ask authors/readers for more resources
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. (C) 2015 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available