Systematic literature review: Machine learning techniques (machine learning)

被引:0
作者
Alfaro, Anderson Damian Jimenez [1 ]
Ospina, Jose Vicente Diaz [2 ]
机构
[1] Univ Catolica Luis Amigo, Ingn Ind, Medellin, Colombia
[2] Univ Catolica Luis Amigo, Ingn Financiero & Negocios, Medellin, Colombia
来源
CUADERNO ACTIVA | 2021年 / 13期
关键词
Machine learning; forecasting; business intelligence; marketing; business management;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Currently, there are a great diversity of models that allow making predictions, and for this there are machine learning techniques that can help organizations to boost their sales through these predictive models. In this article a specialized search of scientific literature is carried out that provides clarity on which are the most used techniques and under what criteria are they effective. According to the research needs, the most relevant articles have been filtered and selected to elucidate how to execute a machine learning project for sales forecasting. From the review carried out, it can be affirmed that the different machine learning techniques found in the literature are evolutions of different known techniques, which is an important component to maintain business competitiveness, and if they are well used could become sales-enhancing tools in companies organizations.
引用
收藏
页码:113 / 121
页数:9
相关论文
共 26 条
[1]  
Aggarwal D., 2019, International Journal of Recent Technology and Engineering, V8, P496
[2]  
Ahmad G. I., 2019, INT J RECENT TECHNOL, V8, P3630
[3]  
Aruna Flarence R., 2018, PERIOD ENG NAT SCI, V6, P201
[4]  
Athey S., 2019, MACHINE LEARNING MET
[5]   Data Mining and Analytics in the Process Industry: The Role of Machine Learning [J].
Ge, Zhiqiang ;
Song, Zhihuan ;
Deng, Steven X. ;
Huang, Biao .
IEEE ACCESS, 2017, 5 :20590-20616
[6]   Diversity in Machine Learning [J].
Gong, Zhiqiang ;
Zhong, Ping ;
Hu, Weidong .
IEEE ACCESS, 2019, 7 :64323-64350
[7]  
Henrique BM., 2018, The Journal of Finance and Data Science, V4, P183, DOI [DOI 10.1016/J.JFDS.2018.04.003, 10.1016/j.jfds.2018.04.003]
[8]   Supervised or unsupervised learning? Investigating the role of pattern recognition assumptions in the success of binary predictive prescriptions [J].
Jafari-Marandi, Ruholla .
NEUROCOMPUTING, 2021, 434 :165-193
[9]  
Jayanthy B., 2019, INT J RECENT TECHNOL, V8, P7020
[10]  
Lopes R. G., 2017, Advances in Science, Technology and Engineering Systems Journal, V2, P1432, DOI [10.25046/aj0203179, DOI 10.25046/AJ0203179]