Machine Learning Based Approaches for Short Term Sales Forecasting in E-Commerce

Altuncu M. A., Tastan M. H., Özcan T.

22nd International Symposium for Production Research, ISPR 2022, Antalya, Turkey, 6 - 08 October 2022, pp.16-24 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-031-24457-5_2
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.16-24
  • Keywords: E-Commerce, Linear regression, LSTM, Sales forecasting, SVR
  • Istanbul Technical University Affiliated: Yes


It is very important for companies with high inventory turnover to be able to efficiently carry out sales and raw material purchases in their trade processes. For this reason, it is very important to be able to predict their short-term sales to execute their own plans in the most effective way. In this study, LSTM, SVR and LR models are proposed to predict short-term sales of companies. For this purpose, 6-month data of a retail company operating in B2B was used. First, to get a more effective result in hourly forecasts, the data, which is a 2-dimensional array, was used in such a way that it would be effective in the last 24 h by including the rolling mechanism in the model. Then, LSTM, SVR and LR models were applied using the dataset developed with the rolling mechanism. The results of the analysis show that, although close to each other, the LSTM model captures the patterns better and that the use of this model can be used as a different option in the management of companies’ short-term sales.