A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. (October 2016)

• Record Type: Journal Article
• Authors: Lu, Chi-Jie; Kao, Ling-Jing
• Is Part Of: Engineering applications of artificial intelligence. Volume 55(2016:Jul.)
• Page Start: 231
• Page End: 238
• Publication Date: 2016-10
• Subjects: Sales forecasting -- Clustering -- Ensemble learning -- Linkage method -- Extreme learning machine -- Computer server; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.06.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Sales forecasting has long been crucial for companies since it is important for financial planning, inventory management, marketing, and customer service. In this study, a novel clustering-based sales forecasting scheme that uses an extreme learning machine (ELM) and assembles the results of linkage methods is proposed. The proposed scheme first uses the K-means algorithm to divide the training sales data into multiple disjointed clusters. Then, for each cluster, the ELM is applied to construct a forecasting model. Finally, a test datum is assigned to the most suitable cluster identified according to the result of combining five linkage methods. The constructed ELM model corresponding to the identified cluster is utilized to perform the final prediction. Two real sales datasets of computer servers collected from two multinational electronics companies are used to illustrate the proposed model. Empirical results showed that the proposed clustering-based sales forecasting scheme statistically outperforms eight benchmark models, and hence demonstrates that the proposed approach is an effective alternative for sales forecasting.