COMPARATIVE STUDY ON RETAIL SALES FORECASTING BETWEEN SINGLE AND COMBINATION METHODS
| Autor: | İpek Deveci Kocakoç, Serkan Aras, Cigdem Polat |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Economics and Econometrics
HF5001-6182 Computer science 02 engineering and technology ARIMA sales forecasting 01 natural sciences retail sales 010104 statistics & probability Retail sales 0202 electrical engineering electronic engineering information engineering Econometrics State space Business Operations management Isolation (database systems) Autoregressive integrated moving average 0101 mathematics ANFIS Adaptive neuro fuzzy inference system Artificial neural network Exponential smoothing combined forecasts neural networks exponential smoothing ARFIMA Business Management and Accounting (miscellaneous) 020201 artificial intelligence & image processing Autoregressive fractionally integrated moving average |
| Zdroj: | Journal of Business Economics and Management, Vol 18, Iss 5 (2017) Journal of Business Economics and Management; Vol 18 No 5 (2017); 803–832 |
| ISSN: | 2029-4433 1611-1699 |
| DOI: | 10.3846/16111699.2017.1367324 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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