Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting

Yıl 2019, Cilt: 7 Sayı: 1, 20 - 26, 31.01.2019

Cagatay Catal Kaan Ece Begum Arslan Akhan Akbulut

https://doi.org/10.17694/bajece.494920

Cited By: 15

Öz

Predicting the sales amount as close as to the
actual sales amount can provide many benefits to companies. Since the fashion
industry is not easily predictable, it is not straightforward to make an
accurate prediction of sales.  In this
study, we applied not only regression methods in machine learning, but also
time series analysis techniques to forecast the sales amount based on several
features. We applied our models on Walmart sales data in Microsoft Azure
Machine Learning Studio platform. The following regression techniques were
applied: Linear Regression, Bayesian Regression, Neural Network Regression,
Decision Forest Regression and Boosted Decision Tree Regression. In addition to
these regression techniques, the following time series analysis methods were implemented:
Seasonal ARIMA, Non-Seasonal ARIMA, Seasonal ETS, Non -Seasonal ETS, Naive
Method, Average Method and Drift Method. It was shown that Boosted Decision Tree
Regression provides the best performance on this sales data. This project is a
part of the development of a new decision support system for the retail
industry.

Anahtar Kelimeler

Sales forecasting, regression, machine learning, time series analysis

Kaynakça

• [1] Kuo, R. J. (2001). A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129(3), 496-517.
• [2] Chen, F. L., & 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), 1336-1345
• [3] Zhao, J., Tang, W., Fang, X., Wang, J., Liu, J., Ouyang, H., ... & Qiang, J. (2015, September). A Novel Electricity Sales Forecasting Method Based on Clustering, Regression and Time Series Analysis. In Proceedings of the 2015 International Conference on Artificial Intelligence and Software Engineering.,
• [4] Tian, Y., Liu, Y., Xu, D., Yao, T., Zhang, M., & Ma, S. (2012, April). Incorporating Seasonal Time Series Analysis with Search Behavior Information in Sales Forecasting. In Proceedings of the 21st International Conference on World Wide Web (pp. 615-616).
• [5] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
• [6] Pandey, A., & Somani, R. K. (2013). A Cloud Computing Based Sales Forecasting System for Small and Medium Scale Textile Industries. computing, 3(4).
• [7] Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342.
• [8] Vijayalakshmi, M., Menezes, B., Menon, R., Divecha, A., Ravindran, R., & Mehta, K. (2010, October). Intelligent sales forecasting engine using genetic algorithms. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1669-1672). ACM.
• [9] Yeo, J., Kim, S., Koh, E., Hwang, S. W., & Lipka, N. (2016, April). Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 133-134). International World Wide Web Conferences Steering Committee.
• [10] Choi, T. M., Yu, Y., & Au, K. F. (2011). A hybrid SARIMA wavelet transform method for sales forecasting. Decision Support Systems, 51(1), 130-140.
• [11] Chang, P. C., Liu, C. H., & Fan, C. Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(5), 344-355.
• [12] Wong, W. K., & Guo, Z. X. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. International Journal of Production Economics, 128(2), 614-624.
• [13] Katkar, V., Gangopadhyay, S. P., Rathod, S., & Shetty, A. (2015, January). Sales forecasting using data warehouse and Naïve Bayesian classifier. In Pervasive Computing (ICPC), 2015 International Conference on (pp. 1-6). IEEE
• [14] Müller-Navarra, M., Lessmann, S., & Voß, S. (2015, January). Sales Forecasting with Partial Recurrent Neural Networks: Empirical Insights and Benchmarking Results. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 1108-1116). IEEE.
• [15] Gao, M., Xu, W., Fu, H., Wang, M., & Liang, X. (2014, July). A novel forecasting method for large-scale sales prediction using extreme learning machine. In Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on (pp. 602-606). IEEE.
• [16] Omar, H. A., & Liu, D. R. (2012, January). Enhancing sales forecasting by using neuro networks and the popularity of magazine article titles. In 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC) (pp. 577-580).
• [17] Lu, C. J., Lee, T. S., & Lian, C. M. (2010, December). Sales forecasting of IT products using a hybrid MARS and SVR model. In 2010 IEEE International Conference on Data Mining Workshops (pp. 593-599)..
• [18] Stojanović, N., Soldatović, M., & Milićević, M. (2014, June). Walmart Recruiting–Store Sales Forecasting. In Proceedings of the XIV International Symposium Symorg 2014: New Business Models and Sustainable Competitiveness (p. 135). Fon.