Machine Learning based Business Forecasting

Full Text (PDF, 1728KB), PP.40-51

Views: 0 Downloads: 0


D. Asir Antony Gnana Singh 1,* E. Jebamalar Leavline 1 S. Muthukrishnan 2 R. Yuvaraj 2

1. Department of Computer Science and Engineering, Anna University, BIT-Campus, Tiruchirappalli, India

2. Department of Information Technology, Anna University, BIT-Campus, Tiruchirappalli, India

* Corresponding author.


Received: 13 Aug. 2018 / Revised: 1 Sep. 2018 / Accepted: 14 Sep. 2018 / Published: 8 Nov. 2018

Index Terms

Business Forecasting, Machine Learning, Gaussian Process, SMOreg, Multilayer Perceptron


The business sectors directly contribute to the growth of any nation. Moreover, the business is an activity of producing, buying, and selling the goods and services to generate the money. The business directly involves in the gross domestic product (GDP). The business forecasting is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business. However, the business forecasting helps to the business sectors for planning, decision making, resource utilization, business success, etc. Therefore, business forecasting is a pressing need for the growth of any business. In recent past, many researches attempt to carry out the business forecasting using different tools. However, this paper presents the business forecasting for sales data using machine learning technique and the obtained results are presented and discussed..

Cite This Paper

D. Asir Antony Gnana Singh, E. Jebamalar Leavline, S. Muthukrishnan, R. Yuvaraj, "Machine Learning based Business Forecasting", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.6, pp. 40-51, 2018. DOI:10.5815/ijieeb.2018.06.05


[1]Yu, X., Qi, Z. and Zhao, Y., 2013. Support vector regression for newspaper/magazine sales forecasting. Procedia Computer Science, 17, pp.1055-1062.
[2]Kaneko, Y., Miyazaki, S. and Yada, K., 2017. The Influence of Customer Movement between Sales Areas on Sales Amount: A Dynamic Bayesian Model of the In-store Customer Movement and Sales Relationship. Procedia Computer Science, 112, pp.1845-1854.
[3]D. Asir Antony Gnana Singh, E. Jebamalar Leavline, S. Muthukrishnan, R. Yuvaraj, November 17 Volume 3 Issue 11, “Regression Based Sales Data Forecasting for Predicting the Business Performance”, International Journal on Future Revolution in Computer Science & Communication Engineering (IJFRSCE), PP: 589 - 593
[4]Chen, F.L. and Ou, T.Y., 2011. Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), pp.1336-1345.
[5]Choi, T.M., Hui, C.L., Liu, N., Ng, S.F. and Yu, Y., 2014. Fast fashion sales forecasting with limited data and time. Decision Support Systems, 59, pp.84-92.
[6]Lu, C.J., 2014. Sales forecasting of computer products based on variable selection scheme and support vector regression. Neurocomputing, 128, pp.491-499.
[7]Clark, T.E. and Ravazzolo, F., 2015. Macroeconomic Forecasting Performance under Alternative Specifications of TimeVarying Volatility. Journal of Applied Econometrics, 30(4), pp.551-575.
[8]Kulkarni, G., Kannan, P.K. and Moe, W., 2012. Using online search data to forecast new product sales. Decision Support Systems, 52(3), pp.604-611.
[9]Fan, Z.P., Che, Y.J. and Chen, Z.Y., 2017. Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, pp.90-100.
[10]Orbach, Y. and Fruchter, G.E., 2011. Forecasting sales and product evolution: The case of the hybrid/electric car. Technological Forecasting and Social Change, 78(7), pp.1210-1226.
[11]Schneider, M.J. and Gupta, S., 2016. Forecasting sales of new and existing products using consumer reviews: A random projections approach. International Journal of Forecasting, 32(2), pp.243-256.
[12]Karvelis, P., Kolios, S., Georgoulas, G. and Stylios, C., 2017, October. Ensemble learning for forecasting main meteorological parameters. In Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on (pp. 3711-3714). IEEE.
[13]Quan, H., Srinivasan, D. and Khosravi, A., 2014. Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE transactions on neural networks and learning systems, 25(2), pp.303-315.
[14]Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H. and Saidur, R., 2014. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, pp.102-109.
[15]Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F.J. and Antonanzas-Torres, F., 2016. Review of photovoltaic power forecasting. Solar Energy, 136, pp.78-111.
[16]Hong, T. and Fan, S., 2016. Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, 32(3), pp.914-938.
[17]Jain, R.K., Smith, K.M., Culligan, P.J. and Taylor, J.E., 2014. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, pp.168-178.
[18]Chen, X.Y., Chau, K.W. and Busari, A.O., 2015. A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence, 46, pp.258-268.
[19]Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.