Comparison of Sales Prediction in Conventional Insights and Machine Learning Perspective
DOI:
https://doi.org/10.54691/bcpbm.v38i.3952Keywords:
Sales Forecasting; Time Series; Prediction Explanation; Machine Learning; Intelligent System.Abstract
Generally, the predictions of sales volume can be regarded as using the system of the forecasting model to estimate the future sales quantity and amount for products and services. Accurate sales forecasting based on the previous sales situation can promote enterprise do better in the future income and encourage enterprises to establish and maintain a highly efficient sales management teamThis paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models which related to the perspective of machine learning, and then compare them. According to the analysis, the two sales forecasting methods have their own advantages and disadvantages. In the future, enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises. These results shed light on guiding further exploration of choosing appropriate sales model for enterprises.
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References
J. Yu and X. Le, Sales forecast for amazon sales based on different statistics methodologies, ICEME, vol. 12, 2016.
C. Chris, The analysis of time series: an introduction, CRC press, 2013.
A. P. Ansuj. Sales forecasting using time series and neural networks, Computers & Industrial Engineering, vol. 31, no. 1-2, 1996.
J. M. P. Menezes Jr. and G. A. Barreto, Long-term time series prediction with the NARX network: An empirical evaluation, Neurocomputing, vol. 71, no. 16-18, pp. 3335–3343, 2008.Y
Y. Miao, Research on Mid - Season Sales Forecast Based on Machine Learning Theory, Zhejiang University of Technology, 2015.
P. Doganis, A. Alexandridis, P. Patrinos, and H. Sarimveis, Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing, Journal of Food Engineering, vol. 75, no. 2, pp. 196–204, 2006.
Thiesing and Vornberger. Sales forecasting using neural networks. Proc. of the International Conference on Computational Intelligence, Theory and Applications, vol. 4, pp. 321-328, 1997.
G. P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, vol. 50, pp. 159-175, 2003.
Y. Weng and H. Feng, Research online store sale forcast model based on BP neural network, Journal of Minjiang University, 2016.
M. Qin and Z. Du, Red tide time series forecasting by combining ARIMA and deep belief network, Elsevier Science Publishers, 2017.
R. Gamberini, F. Lolli, B. Rimini, and F. Sgarbossa, Forecasting of sporadic demand patterns with seasonality and trend components: An empirical comparison between holt-winters and (s)ARIMA methods, Mathematical Problems in Engineering, vol. 2010, Article ID 579010, 15 pages, 2010.






