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Comparative study on retail sales forecasting between single and combination methods

Journal of Research in Business, Economics and Management

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Title Comparative study on retail sales forecasting between single and combination methods
 
Creator Aras, Serkan
Kocakoç, İpek Deveci
Polat, Cigdem
 
Description In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system.
 
Publisher VGTU Press Technika
 
Date 2017-10-27
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journals.vgtu.lt/index.php/JBEM/article/view/1265
10.3846/16111699.2017.1367324
 
Source Journal of Business Economics and Management; Vol 18 No 5 (2017); 803–832
2029-4433
1611-1699
 
Language eng
 
Relation https://journals.vgtu.lt/index.php/JBEM/article/view/1265/995