ZAMAN SERİSİANALİZİNDE MLP YAPAY SİNİR AĞLARI VE ARIMA MODELİNİN KARŞILAŞTIRILMASI
Yıl 2009,
Sayı: 33, 161 - 172, 16.05.2015
Oğuz Kaynar
Serkan Taştan
Öz
Bu çalışmada zaman serisi analizinde yaygın olarak kullanılan Box-Jenkis modelleri ile ileri beslemeli yapay sinir ağlarının bir karşılaştırması yapılmıştır. Veri seti olarak aylık ve günlük döviz (YTL/$) kuru verileri kullanılmıştır. Farklı Box-Jenkins ve yapay sinir ağları modelleri oluşturulmuş, her bir teknik için en iyi sonuçları veren modeller seçilerek karşılaştırma yapılmış-tır. Elde edilen sonuçlar Yapay sinir ağlarının finansal verilerin tahmininde kullanılabilecek başarılı bir yöntem olduğunu göstermiştir
Kaynakça
- FAUSETT, Laurene; (1994), Fundamentals of Neural Networks: Architec- tures, Algorithms and Applications, Prentice Hall.
- HANN, Tae Horn ve STEURER, Elmar; (1996), “Much Ado About Nothing? Exchange Rate Forecasting: Neural Networks Vs. Linear Models Using Monthly And Weekly Data”, Neurocomputing, Vol. 10, ss.323–339.
- KAMRUZZAMMAN, Joarder ve SARKER, Ruhul A.; (2003), “Forecasting of Currency Exchange Rates using ANN: A Case Study”, Proc. IEEE International Conference on Neural Networks & Signal Processing (ICNNSP03), Nanjing, China, ss.793-797.
- KUAN, Chung Ming ve LIU, Tung; (1995), “Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks”, Journal of Applied Econometrics, Vol. 10, pp. 347–364.
- ÖZTEMEL, Ercan; (2003), Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
- TANG, Zaiyong ve FISHWICK, Paul A.; (1993), “Feedforward Neural Nets As Models For Time Computing, 5(4), ss.374–385. Series
- Forecasting”,ORSA Journal on
- WEIGEND, Andreas S., HUBERMAN, Barnardo A. ve RUMELHART, David E.; (1992), “Predicting sunspots and exchange rates with connectionist Networks In Nonlinear modeling and forecasting”, Addison-Welsey, ss. 395–432.
- WU, Berlin; (1995), “Model-Free Forecasting For Nonlinear Time Series (With Application to Exchange Rates”, Computational Statistics and Data Analysis, Vol.19, ss. 433–459.
- ZHANG, Gioqinang ve HU, Michael Y. ;(1998), “Neural Network Forecasting of the British Pound/US Dollar Exchange Rate”, OMEGA: Int. Journal of Management Science, Vol. 26, ss. 495-506.
- ZHANG, Gioqinang, PATUWO, B. Eddy ve HU, Michael Y., (1998) “Forecasting With Artificial Neural Networks: The State Of The Art”, International Journal of Forecasting, Vol.14, ss.35-62.
Yıl 2009,
Sayı: 33, 161 - 172, 16.05.2015
Oğuz Kaynar
Serkan Taştan
Kaynakça
- FAUSETT, Laurene; (1994), Fundamentals of Neural Networks: Architec- tures, Algorithms and Applications, Prentice Hall.
- HANN, Tae Horn ve STEURER, Elmar; (1996), “Much Ado About Nothing? Exchange Rate Forecasting: Neural Networks Vs. Linear Models Using Monthly And Weekly Data”, Neurocomputing, Vol. 10, ss.323–339.
- KAMRUZZAMMAN, Joarder ve SARKER, Ruhul A.; (2003), “Forecasting of Currency Exchange Rates using ANN: A Case Study”, Proc. IEEE International Conference on Neural Networks & Signal Processing (ICNNSP03), Nanjing, China, ss.793-797.
- KUAN, Chung Ming ve LIU, Tung; (1995), “Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks”, Journal of Applied Econometrics, Vol. 10, pp. 347–364.
- ÖZTEMEL, Ercan; (2003), Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul.
- TANG, Zaiyong ve FISHWICK, Paul A.; (1993), “Feedforward Neural Nets As Models For Time Computing, 5(4), ss.374–385. Series
- Forecasting”,ORSA Journal on
- WEIGEND, Andreas S., HUBERMAN, Barnardo A. ve RUMELHART, David E.; (1992), “Predicting sunspots and exchange rates with connectionist Networks In Nonlinear modeling and forecasting”, Addison-Welsey, ss. 395–432.
- WU, Berlin; (1995), “Model-Free Forecasting For Nonlinear Time Series (With Application to Exchange Rates”, Computational Statistics and Data Analysis, Vol.19, ss. 433–459.
- ZHANG, Gioqinang ve HU, Michael Y. ;(1998), “Neural Network Forecasting of the British Pound/US Dollar Exchange Rate”, OMEGA: Int. Journal of Management Science, Vol. 26, ss. 495-506.
- ZHANG, Gioqinang, PATUWO, B. Eddy ve HU, Michael Y., (1998) “Forecasting With Artificial Neural Networks: The State Of The Art”, International Journal of Forecasting, Vol.14, ss.35-62.