Araştırma Makalesi
Yıl 2018,
Cilt: 8 Sayı: 15, 148 - 169, 22.06.2018
Öz
Firmaların üst yönetimi ve tüm departmanları, planlama ve karar
alma sürecinde, satış tahminine yönelik verilere ihtiyaç duymaktadırlar. Bu
çalışmada paslanmaz çelik sektöründe faaliyet gösteren bir firmanın satış
yaptığı sektörlere göre, satış tahminleri gerçekleştirilmiştir.Bunun için
firmanın veri tabanından Ocak 2008 ile Mart 2016 arasındaki günlük satış
verileri elde edilmiştir. Ham veri setinde bulunan satış hareketleriyle müşteri
bilgileri eşleştirilerek sektörlere ait satış rakamları tespit edilmiştir. Veri
madenciliği yöntemleriyle (Veri Önişleme, Destek Vektör Regresyonu ve Yapay
Sinir Ağları) Toplam satış ve sektörlere göre satış verilerinin tahminleri
gerçekleştirilmiştir.Uygulama sonucunda Destek Vektör Regresyon yönteminin
nispeten daha başarılı olduğu görülmüştür.
Anahtar Kelimeler
Kaynakça
- ALPAYDIN, E. (2011).Yapay Öğrenme. Boğaziçi Üniversitesi Yayınevi,İstanbul
- AKPINAR, H. (2014).DATA Veri Madenciliği – Veri Analizi, Papatya Yayınevi,İstanbul
- CHASE JR, Charles W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
- CHIEN, C. F.and Chen, L. F.(2008). Data Miningto Improve Personnel Selectionand EnhanceHuman Capital: a Case Studyin High-Technology Industry, Expert Systems with Applications, 34: 280-290.
- FRANK E., Wang Y., Inglis S., Holmes G.and Witten I. H.(1998). Using Model Trees for Classification. Mach. Learn. 32(1): 63-76
- HAN J.ve Kamber M.(2012).Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, USA.
- HAND, D., Mannila, H. and Smyth, P.(2001).Principles of Data Mining, The MIT Press, Cambridge, Massachusetts, USA.
- HICHAM, A.ve Mohamed, B. (2012, November). A Modelfor Sales Forecasting Basedon FuzzyClusteringand Back-PropagationNeural Networks WithAdaptive LearningRate. In Complex Systems (ICCS), 2012 International Conference on, IEEE, 1-5.
- JIANG, H.ve He, W. (2012). Grey Relational Grade in Local Support Vector Regression for Financial Time Series Prediction.Expert Systems with Applications,39(3): 2256-2262.
- KAMRUZZAMAN, J. (Ed.). (2006). Artificial Neural Networks In Finance and Manufacturing. IGI Global.KHASHEİ, M., ve Bijari, M. (2010).“An Artificial Neural Network (P,D,Q) Model for Time Series Forecasting“, ScienceDirect Journal of Expert System with Applications,37: 479-489.
- KIM, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines.Neurocomputing,55(1): 307-319.KUO, R.J. ve Xue, K.C. (1998) “An Intelligent Sales Forecasting System Through Integration of Artificial Neural Network and Fuzzy Neural Network”, Computers In Industry 37: 1-15.
- LANTZ, B. (2013). Machine Learning With R. Packt Publishing Ltd.
- MARVUGLIA, A. and Messineo, A.(2012) “Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption”, Journal of Energy Procedia,14: 45-55.
- MEGAHED, A., Yin, P.and Nezhad, H. R. M. (2016, June). An Optimization Approach to Services Sales Forecasting in a Multi-Staged Sales Pipeline. In Services Computing (SCC), 2016 IEEE International Conference on: 713-719, IEEE.
- SİLAHTAROĞLU, G. (2013)"Veri Madenciliği."Papatya Yayınları, İstanbul.
- TOLUN, S. (2008).Destek Vektör Makineleri: Banka Başarısızlığın Tahmini Üzerine Bir Uygulama, İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, 2008
- TUFFERY, S., (2011) Data Mining and Statistics For Decision Making, 1st ed., Wiley, USA
- WRITTEN, I. H. and Frank, E. (2005).Data Mining: Practical Machine Learning Tools and Techniques,Second Edition, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2005.
- VHATKAR, S.ve Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model. Procedia Computer Science, 79: 238-243.
- ZHANG, G., Patuwo, G. E. and Hu, M. Y. (1998).“Forecasting with Artificial Neural Networks: The State of The Arts”, International Journal of Forecasting, 4(1): 35.
- CRISP-DM, SAS Enterprise Miner,
- http://www.sas.com/offices/europe/uk/technologies/analytics/atamining/miner/semma.htm, (erişim tarihi: 01.01.2015)The International Stainless Steel Forum (ISSF)http://www.worldstainless.org/(erişim tarihleri:01.01.2014-06.06.2016)
Yıl 2018,
Cilt: 8 Sayı: 15, 148 - 169, 22.06.2018
Öz
Kaynakça
- ALPAYDIN, E. (2011).Yapay Öğrenme. Boğaziçi Üniversitesi Yayınevi,İstanbul
- AKPINAR, H. (2014).DATA Veri Madenciliği – Veri Analizi, Papatya Yayınevi,İstanbul
- CHASE JR, Charles W. (2013). Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
- CHIEN, C. F.and Chen, L. F.(2008). Data Miningto Improve Personnel Selectionand EnhanceHuman Capital: a Case Studyin High-Technology Industry, Expert Systems with Applications, 34: 280-290.
- FRANK E., Wang Y., Inglis S., Holmes G.and Witten I. H.(1998). Using Model Trees for Classification. Mach. Learn. 32(1): 63-76
- HAN J.ve Kamber M.(2012).Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, USA.
- HAND, D., Mannila, H. and Smyth, P.(2001).Principles of Data Mining, The MIT Press, Cambridge, Massachusetts, USA.
- HICHAM, A.ve Mohamed, B. (2012, November). A Modelfor Sales Forecasting Basedon FuzzyClusteringand Back-PropagationNeural Networks WithAdaptive LearningRate. In Complex Systems (ICCS), 2012 International Conference on, IEEE, 1-5.
- JIANG, H.ve He, W. (2012). Grey Relational Grade in Local Support Vector Regression for Financial Time Series Prediction.Expert Systems with Applications,39(3): 2256-2262.
- KAMRUZZAMAN, J. (Ed.). (2006). Artificial Neural Networks In Finance and Manufacturing. IGI Global.KHASHEİ, M., ve Bijari, M. (2010).“An Artificial Neural Network (P,D,Q) Model for Time Series Forecasting“, ScienceDirect Journal of Expert System with Applications,37: 479-489.
- KIM, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines.Neurocomputing,55(1): 307-319.KUO, R.J. ve Xue, K.C. (1998) “An Intelligent Sales Forecasting System Through Integration of Artificial Neural Network and Fuzzy Neural Network”, Computers In Industry 37: 1-15.
- LANTZ, B. (2013). Machine Learning With R. Packt Publishing Ltd.
- MARVUGLIA, A. and Messineo, A.(2012) “Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption”, Journal of Energy Procedia,14: 45-55.
- MEGAHED, A., Yin, P.and Nezhad, H. R. M. (2016, June). An Optimization Approach to Services Sales Forecasting in a Multi-Staged Sales Pipeline. In Services Computing (SCC), 2016 IEEE International Conference on: 713-719, IEEE.
- SİLAHTAROĞLU, G. (2013)"Veri Madenciliği."Papatya Yayınları, İstanbul.
- TOLUN, S. (2008).Destek Vektör Makineleri: Banka Başarısızlığın Tahmini Üzerine Bir Uygulama, İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, 2008
- TUFFERY, S., (2011) Data Mining and Statistics For Decision Making, 1st ed., Wiley, USA
- WRITTEN, I. H. and Frank, E. (2005).Data Mining: Practical Machine Learning Tools and Techniques,Second Edition, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2005.
- VHATKAR, S.ve Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model. Procedia Computer Science, 79: 238-243.
- ZHANG, G., Patuwo, G. E. and Hu, M. Y. (1998).“Forecasting with Artificial Neural Networks: The State of The Arts”, International Journal of Forecasting, 4(1): 35.
- CRISP-DM, SAS Enterprise Miner,
- http://www.sas.com/offices/europe/uk/technologies/analytics/atamining/miner/semma.htm, (erişim tarihi: 01.01.2015)The International Stainless Steel Forum (ISSF)http://www.worldstainless.org/(erişim tarihleri:01.01.2014-06.06.2016)
Toplam 22 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | İktisat |
| Yazarlar | |
| Yayımlanma Tarihi | 22 Haziran 2018 |
| Kabul Tarihi | 22 Haziran 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 8 Sayı: 15 |