COMPARATIVE STUDY ON RETAIL SALES FORECASTING BETWEEN SINGLE AND COMBINATION METHODS

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ARAS S., DEVECİ KOCAKOÇ İ., Polat C.

JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT, cilt.18, sa.5, ss.803-832, 2017 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 5
  • Basım Tarihi: 2017
  • Doi Numarası: 10.3846/16111699.2017.1367324
  • Dergi Adı: JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.803-832
  • Anahtar Kelimeler: sales forecasting, neural networks, exponential smoothing, ARIMA, ARFIMA, ANFIS, combined forecasts, retail sales, ARTIFICIAL NEURAL-NETWORKS, FUZZY INFERENCE SYSTEM, FINANCIAL TIME-SERIES, OF-THE-ART, COMBINING FORECASTS, STATE-SPACE, MODELS, SELECTION, DEMAND, ANFIS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.