Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.494920 Engineering Mühendislik Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting Catal Cagatay Wageningen University Ece Kaan Istanbul Kultur University Arslan Begum Istanbul Kultur University Akbulut Akhan North Carolina State University 01 31 2019 7 1 20 26 12 10 2018 01 17 2019 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Predicting the sales amount as close as to theactual sales amount can provide many benefits to companies. Since the fashionindustry is not easily predictable, it is not straightforward to make anaccurate prediction of sales. In thisstudy, we applied not only regression methods in machine learning, but alsotime series analysis techniques to forecast the sales amount based on severalfeatures. We applied our models on Walmart sales data in Microsoft AzureMachine Learning Studio platform. The following regression techniques wereapplied: Linear Regression, Bayesian Regression, Neural Network Regression,Decision Forest Regression and Boosted Decision Tree Regression. In addition tothese regression techniques, the following time series analysis methods were implemented:Seasonal ARIMA, Non-Seasonal ARIMA, Seasonal ETS, Non -Seasonal ETS,NaiveMethod, Average Method and Drift Method. It was shown that Boosted Decision TreeRegression provides the best performance on this sales data. This project is apart of the development of a new decision support system for the retailindustry.

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