Yıl 2019, Cilt 7, Sayı 1, Sayfalar 20 - 26 2019-01-31

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

Cagatay Catal [1] , Kaan Ece [2] , Begum Arslan [3] , Akhan Akbulut [4]

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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.

Sales forecasting, regression, machine learning, time series analysis
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Birincil Dil en
Konular Mühendislik
Dergi Bölümü Araştırma Makalesi
Yazarlar

Yazar: Cagatay Catal (Sorumlu Yazar)
Kurum: Wageningen University
Ülke: The Netherlands


Yazar: Kaan Ece
Kurum: Istanbul Kultur University
Ülke: Turkey


Yazar: Begum Arslan
Kurum: Istanbul Kultur University
Ülke: Turkey


Yazar: Akhan Akbulut
Kurum: North Carolina State University
Ülke: United States


Bibtex @araştırma makalesi { bajece494920, journal = {Balkan Journal of Electrical and Computer Engineering}, issn = {2147-284X}, address = {Balkan Yayın}, year = {2019}, volume = {7}, pages = {20 - 26}, doi = {10.17694/bajece.494920}, title = {Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting}, key = {cite}, author = {Catal, Cagatay and Ece, Kaan and Arslan, Begum and Akbulut, Akhan} }
APA Catal, C , Ece, K , Arslan, B , Akbulut, A . (2019). Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting. Balkan Journal of Electrical and Computer Engineering, 7 (1), 20-26. DOI: 10.17694/bajece.494920
MLA Catal, C , Ece, K , Arslan, B , Akbulut, A . "Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting". Balkan Journal of Electrical and Computer Engineering 7 (2019): 20-26 <http://dergipark.gov.tr/bajece/issue/42931/494920>
Chicago Catal, C , Ece, K , Arslan, B , Akbulut, A . "Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting". Balkan Journal of Electrical and Computer Engineering 7 (2019): 20-26
RIS TY - JOUR T1 - Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting AU - Cagatay Catal , Kaan Ece , Begum Arslan , Akhan Akbulut Y1 - 2019 PY - 2019 N1 - doi: 10.17694/bajece.494920 DO - 10.17694/bajece.494920 T2 - Balkan Journal of Electrical and Computer Engineering JF - Journal JO - JOR SP - 20 EP - 26 VL - 7 IS - 1 SN - 2147-284X- M3 - doi: 10.17694/bajece.494920 UR - http://dx.doi.org/10.17694/bajece.494920 Y2 - 2019 ER -
EndNote %0 Balkan Journal of Electrical and Computer Engineering Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting %A Cagatay Catal , Kaan Ece , Begum Arslan , Akhan Akbulut %T Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting %D 2019 %J Balkan Journal of Electrical and Computer Engineering %P 2147-284X- %V 7 %N 1 %R doi: 10.17694/bajece.494920 %U 10.17694/bajece.494920
ISNAD Catal, Cagatay , Ece, Kaan , Arslan, Begum , Akbulut, Akhan . "Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting". Balkan Journal of Electrical and Computer Engineering 7 / 1 (Ocak 2019): 20-26. http://dx.doi.org/10.17694/bajece.494920