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Published in:
2021 | OriginalPaper | Chapter
The Demand Forecasting Method Based on Least Square Support Vector Machine
Authors : Jing Liu, Tongfei Shang, Jingwei Yang, Jie Wu
Published in: Big Data Analytics for Cyber-Physical System in Smart City
Publisher: Springer Singapore
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Abstract
Support vector machine seeks the best compromise between model complexities and learning ability based on limited sample information, in order to obtain the best generalization ability, and has achieved good application effects in demand forecasting. In this paper, a least squares support vector machine is used to classify various factors that affect the consumption of ammunition. Experts will evaluate and score each factor. After preprocessing the data, the projection pursuit method is used to reduce the dimension of the evaluation data. The comprehensive evaluation index is obtained and used as an input item, and the actual consumption is taken as the ideal output to realize the forecast of ammunition demand. The simulation results prove the effectiveness of the method in this paper.