A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Fatih YİĞİT; Şakir ESNAF; Bahar YALÇIN KAVUŞ

doi:https://doi.org/10.34110/forecasting.957494

Makale detayları

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Yazarlar:

Fatih YİĞİT | Şakir ESNAF | Bahar YALÇIN KAVUŞ

Yayıncı:

Giresun University

Ana Başlıklar:

Matematik

Dergi:

Turkish Journal of Forecasting 2021 Vol.05 Issue 2, pp.23-35

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000637101

Sıklık:

İki Yıllık

eISSN:

2618-6594

DOI:

https://doi.org/10.34110/forecasting.957494

Özet:

 

Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand forecasting has a direct impact on the performance of a company. The goal of the study is to present a new two-stage combination model named Hybrid-2-Best, for accurate demand forecasting. The model combines three forecasting models in a single combined forecast. The Hybrid-2-Best model uses a two-stage algorithm to achieve better-performing forecasts. Case study showed that the proposed Hybrid-2-Best model performs the best forecast performance among other combination techniques and individual methods. Furthermore, GP integration in the first and second stages gives flexibility. Experimental results indicate that the proposed Hybrid-2-Best model is a promising alternative for sales demand forecasting. MAPE of the proposed model is 0,13. This is a good result and better than compared other models. Proposed model performed better than other compared models in MASE and MSE as well.

Makale detayları

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Yazarlar: Fatih YİĞİT | Şakir ESNAF | Bahar YALÇIN KAVUŞ

Yayıncı: Giresun University

Ana Başlıklar: Matematik

Dergi: Turkish Journal of Forecasting 2021 Vol.05 Issue 2, pp.23-35

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000637101

Sıklık: İki Yıllık

eISSN: 2618-6594

DOI: https://doi.org/10.34110/forecasting.957494

Sayı

Yazarlar:

Fatih YİĞİT | Şakir ESNAF | Bahar YALÇIN KAVUŞ

Yayıncı:

Giresun University

Ana Başlıklar:

Matematik

Dergi:

Turkish Journal of Forecasting 2021 Vol.05 Issue 2, pp.23-35

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000637101

Sıklık:

İki Yıllık

eISSN:

2618-6594

DOI:

https://doi.org/10.34110/forecasting.957494