Abstract
Bu çalışma Box-Jenkins ile tahmin
yapmak için, sözkonusu tekniğe ait belirli adımlarda alternatif olarak Yapay
Sinir Ağları Yapay Zeka tekniğini kullanmaya yöneliktir. Bu amaçla içinde
eğitilmiş ağları kullanan bir bilgisayar algoritması geliştirilmiştir.
Geliştirilen algoritmanın sonuçlarının kıyaslanabilmesi için, Box-Jenkins
tekniğini bilinen (klasik) şekliyle kullanan programlar içinden Statistica for
Windows seçilmiştir. Hazırlanan test setleri Statistica programı ve
geliştirilen algoritma ile ayrı ayrı işleme sokulmuş ve elde edilen sonuçlar
karşılaştırılmıştır.
References
- [1] P. Newbold and C.W.J. Granger, “Experience with Forecasting, Univariate Time Series and the Combination of Forecast”, Journal of Royal Statistical Society, Vol.37, p.75, 1974.
- [2] V.A. Mabert and R.C. Radcliffe, “A Forecasting Methodology as Applied to Financial Time Series”, The Accounting Review, Vol.49, p.62, 1974.
- [3] H. Kaya, “İstatistiksel Ön Tahmin Yöntemleri”, Ankara H.Ü. İkt. ve İd. Bil. Fak. Yayınları, 1985.
- [4] G.E.P. Box, and G.M. Jenkins, “Time Series Analysis: Forecasting and Control”, California Holden-Day Inc., 1976.
- [5] S. Makridakis, S.C. Wheelwright and V.E. McGee, “Forecasting: Methods and Application”, New York Jhon Wiley & Sons, 1983.
- [6] A. Özmen, “Mevsimsel Dalgalanmalar İçermeyen Zaman Serilerinde Kısa Dönem Öngörü Amaçlı Box-Jenkins (ARIMA) Modellerinin Kullanımı”, Anadolu Üniv. Fen-Edebiyat Fakültesi Dergisi (İstatistik), C:2, No.1, s.106, 1989.
- [7] J. C. Chambers, S.K. Mullick and D.D. Smith, “How to Choose the Right Forecasting Technique”, Harvard Business Review, pp.45-74, 1971.
- [8] T.H. Naylor, T.G. Seaks and D.W. Wichern, “Box-Jenkins Methods: An Alternative to Econometric Models”, International Statistical Review, Vol.40, s.125, 1972.
- [9] L.A. Johnson and D.C. Montgomery, “Operations Research in Production Planning, Scheduling and Inventory Control”, New York John Wiley & Sons Inc., p.466, 1974.
- [10] D.C. Montgomery and L.A. Johnson, “Forecasting and Time Series Analysis”, New York McGraw-Hill, p.206, 1976.
- [11] K.J. Hunt, D. Sbarbaro, R. Zbikowski and P.J. Gawthrop, “Neural Network for Control System-A Survey”, Automatica, Vol:28, No:6, pp.1083-1112, 1992.
- [12] T. Fukuda and T. Shibata, “Theory and Applications of Neural Networks for Industrial Control Systems”, IEEE Trans. on Industrial Electronics, Vol:39, No:6, pp.472-489, 1992.
- [13] E. Öztemel, ”Integrating Expert Systems and Neural Networks for Intelligent on-line Statistical Process Control”, PhD Dissertation, University of Wales, 1992.
- [14] H.B. Hwrang and N.F. Hubele, “X-Bar Chart Pattern Recognition Using Neural Nets”, In: 45th Annual Quality Congress. American Society for Quality Control, Milwaukee, pp.884-889, 1991.
- [15] D.T. Pham and E. Öztemel, “An Integrated Neural Network and Expert System Tool For Statistical Process Control”, Proc. Mech. Engrs, Vol:209, pp.91-97, 1995.
- [16] S.K. Sim, K.T. Veo and W.H. Lee, “An Expert Neural Network System for Dynamic Job Shop Scheduling”, Int.J. Prod.Res., Vol:32, No:8, pp.1759-1773, 1994.
- [17] E. Öztemel, “Yapay Sinir Ağları”, Papatya Yayıncılık, İstanbul, 2003.
- [18] O. Kaynar, S. Taştan, “Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA Modelinin Karşılaştırılması”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı:33, ss.161-172, 2009.
- [19] A. Ötkün, B. Karlık, “YSA ve Pencere Ortalamarı Kullanılarak Yüz Tanıma Sistemi”, Otomatik Kontrol Ulusal Toplantısı, ss.996-1000, 2013.
Two Phased Artificial Neural Network Learning Embedded into Box-Jenkins Modelling for Non-Seasonal Data
Abstract
This study is about making
forecasts with Box-Jenkins method using Artificial Neural Network, Artificial
Intelligence technique in specific steps. For this purpose an algorithm is
developed which is using trained networks. For comparison of developed
algorithm's results, Statistica for windows is choosen from programs using
Box-Jenkins technique in common (classic) way. Prepared test sets, are put in
process seperately with Statistica program and developed algorithm, and the
obtained results are compared.
Keywords
Forecasting Box-Jenkins Forecasting Technique Artificial Neural Network Parameter Estimation
References
- [1] P. Newbold and C.W.J. Granger, “Experience with Forecasting, Univariate Time Series and the Combination of Forecast”, Journal of Royal Statistical Society, Vol.37, p.75, 1974.
- [2] V.A. Mabert and R.C. Radcliffe, “A Forecasting Methodology as Applied to Financial Time Series”, The Accounting Review, Vol.49, p.62, 1974.
- [3] H. Kaya, “İstatistiksel Ön Tahmin Yöntemleri”, Ankara H.Ü. İkt. ve İd. Bil. Fak. Yayınları, 1985.
- [4] G.E.P. Box, and G.M. Jenkins, “Time Series Analysis: Forecasting and Control”, California Holden-Day Inc., 1976.
- [5] S. Makridakis, S.C. Wheelwright and V.E. McGee, “Forecasting: Methods and Application”, New York Jhon Wiley & Sons, 1983.
- [6] A. Özmen, “Mevsimsel Dalgalanmalar İçermeyen Zaman Serilerinde Kısa Dönem Öngörü Amaçlı Box-Jenkins (ARIMA) Modellerinin Kullanımı”, Anadolu Üniv. Fen-Edebiyat Fakültesi Dergisi (İstatistik), C:2, No.1, s.106, 1989.
- [7] J. C. Chambers, S.K. Mullick and D.D. Smith, “How to Choose the Right Forecasting Technique”, Harvard Business Review, pp.45-74, 1971.
- [8] T.H. Naylor, T.G. Seaks and D.W. Wichern, “Box-Jenkins Methods: An Alternative to Econometric Models”, International Statistical Review, Vol.40, s.125, 1972.
- [9] L.A. Johnson and D.C. Montgomery, “Operations Research in Production Planning, Scheduling and Inventory Control”, New York John Wiley & Sons Inc., p.466, 1974.
- [10] D.C. Montgomery and L.A. Johnson, “Forecasting and Time Series Analysis”, New York McGraw-Hill, p.206, 1976.
- [11] K.J. Hunt, D. Sbarbaro, R. Zbikowski and P.J. Gawthrop, “Neural Network for Control System-A Survey”, Automatica, Vol:28, No:6, pp.1083-1112, 1992.
- [12] T. Fukuda and T. Shibata, “Theory and Applications of Neural Networks for Industrial Control Systems”, IEEE Trans. on Industrial Electronics, Vol:39, No:6, pp.472-489, 1992.
- [13] E. Öztemel, ”Integrating Expert Systems and Neural Networks for Intelligent on-line Statistical Process Control”, PhD Dissertation, University of Wales, 1992.
- [14] H.B. Hwrang and N.F. Hubele, “X-Bar Chart Pattern Recognition Using Neural Nets”, In: 45th Annual Quality Congress. American Society for Quality Control, Milwaukee, pp.884-889, 1991.
- [15] D.T. Pham and E. Öztemel, “An Integrated Neural Network and Expert System Tool For Statistical Process Control”, Proc. Mech. Engrs, Vol:209, pp.91-97, 1995.
- [16] S.K. Sim, K.T. Veo and W.H. Lee, “An Expert Neural Network System for Dynamic Job Shop Scheduling”, Int.J. Prod.Res., Vol:32, No:8, pp.1759-1773, 1994.
- [17] E. Öztemel, “Yapay Sinir Ağları”, Papatya Yayıncılık, İstanbul, 2003.
- [18] O. Kaynar, S. Taştan, “Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA Modelinin Karşılaştırılması”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı:33, ss.161-172, 2009.
- [19] A. Ötkün, B. Karlık, “YSA ve Pencere Ortalamarı Kullanılarak Yüz Tanıma Sistemi”, Otomatik Kontrol Ulusal Toplantısı, ss.996-1000, 2013.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | October 1, 2017 |
| Submission Date | August 20, 2017 |
| Published in Issue | Year 2017 Volume: 5 Issue: 3 |