Academic Journal of Computing & Information Science, 2023, 6(13); doi: 10.25236/AJCIS.2023.061305.
Jie Gao1, Lu Zhang1, Bo Lin2, Xunbin Hu1
1Xi'an Company of Shaanxi Tobacco Company, Xi'an, China
2Shaanxi Company of China Tobacco Company, Xi'an, China
The accuracy of supply forecast and sales forecast of tobacco industry is directly related to the production and development of tobacco industry. Supply and sales forecast is the premise and basis of production and sales plan, which can improve the scientific nature of production and sales plan, so as to create more economic benefits for the tobacco industry and promote social and economic development. The application of cloud computing technology and SSA-SVR hybrid model can effectively solve the problem of tobacco industry and improve the sales prediction accuracy of tobacco industry. In this regard, this paper combines the literature data method, case analysis method, statistical methods, comparative experiment and other methods to deeply study the tobacco industry sales forecast based on cloud computing and SSA-SVR.
Tobacco industry, Cloud computing technology, Singular spectrum analysis (SSA), Support vector regression (SVR) model, Drosophila optimization algorithm, SSA-SVR hybrid model, Sales forecast
Jie Gao, Lu Zhang, Bo Lin, Xunbin Hu. Research on Tobacco Industry Sales Forecast Based on Cloud Computing and SSA-SVR. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 29-34. https://doi.org/10.25236/AJCIS.2023.061305.
[1] Wu Mingshan, Wang Bing, et al Research on cigarette sales volume combination prediction model [J]. Chinese Journal of tobacco, 2019, 25 (3): 84-91
[2] Zhao Xinbo. Research on Big Data and Cloud Computing Technology. Management & Technology of SME, 2021, 14(8), 186-187.
[3] He Xuefeng. Exploration of job costing method based on artificial intelligence, big data and cloud computing-- A case study of tobacco companies in China. Finance and Accounting Monthly, 2018, 17(2): 69-72.
[4] Wang Haifei, He Lili. Application of MQT based on Hadoop cloud computing in tobacco marketing decision analysis [J]. Industrial control computer, 2012,25 (12): 101-103
[5] Zhu Feng, Gao Lin. Research on cigarette market demand prediction based on combination model [J]. Cooperative economy and technology, 2017 (1): 62-64
[6] Krishnan, Polak. Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method[C]. // IET and ITS Conference on Road Transport Information and Control: Curran Associates, Inc., 2012:1-6.
[7] Chang Bingguo, Zang Hongying, Liao Chunlei, et al. Retail sales forecast based on selective integrated ARMA combination model [J]. Computer measurement and control, 2018,26 (5): 132-135
[8] Wang Shihao, Zhang Xiaoni, Zhang Yun, et al. Integrated prediction of cigarette demand in Tongchuan City [J]. Chinese Journal of tobacco, 2019,25 (6): 105-109
[9] Portes, Leonardo, Aguirre,et al. Matrix formulation and singular-value decomposition algorithm for structured varimax rotation in multivariate singular spectrum analysis[J]. Physical review, E, 2016,93(5 Pt.A).
[10] Zhong K, Wang Y, Pei J, et al. Super efficiency SBM-DEA and neural network for performance evaluation[J]. Information Processing & Management, 2021, 58(6): 102728.
[11] Jan N, Gwak J, Pei J, et al. Analysis of networks and digital systems by using the novel technique based on complex fuzzy soft information[J]. IEEE Transactions on Consumer Electronics, 2022, 69(2): 183-193.
[12] Yu Z, Pei J, Zhu M, et al. Multi-attribute adaptive aggregation transformer for vehicle re-identification[J]. Information Processing & Management, 2022, 59(2): 102868.
[13] Li J, Li S, Cheng L, et al. BSAS: A Blockchain-Based Trustworthy and Privacy-Preserving Speed Advisory System[J]. IEEE Transactions on Vehicular Technology, 2022, 71(11): 11421-11430