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THE COMPARISON OF ROBUST ARIMA MODEL AND ARTIFICIAL NEURAL NETWORK MODEL: AN EXAMPLE OF TOURISM

Year 2018, Issue: 040, 7 - 12, 15.06.2018

Abstract

Artificial neural network is an analysis procedure which became popular after the first half of twentieth century. Having many successful applications in areas such as Pattern Recognition, Sound Analysis and etc., the artificial neural network procedure has also been used in Time Series Analysis. Nevertheless, the factors that effect the success of time series analysis are different than the factors of success in the areas we mentioned previously and factors such as estimation of the time series’ parameters and the existence of breaking points of time series can effect the forecasts of the model. That’s why in our study we seek to develop the artificial neural network procedure. Robust Statistical Methods in time series analysis, have developed effective time series analysis procedures even though there can be a small amount of data. One of those methods is the modified maximum likelihood estimation method. In this method using any time series model, the forecasts are obtained usingmodified maximum likelihood estimates of parameters and using the distribution of residuals we can obtain unbiased and efficient estimators. In this study, we will compare the modified maximum likelihood method and the enhanced artificial neural network procedure on a tourism data.

References

  • [1] Azadeh, A., Neshat, N., Mardan, E., Saberi, M. (2013). Optimization of steel demand forecasting with complete and uncertain economic inputs by an integrated neural network–fuzzy mathematical programming approach. International Journal of Advanced Manufactured Technology, 65, 833–841.
  • [2] Jhajharia, D., Chattopadhyay, S., Choudhary, R. R., Dev, Vas S., Vijay, P., Lal, S. (2013). Influence of climate on incidences of malaria in the Thar Desert for Northwest India. International Journal of Climatology, 33(2), 312-325.
  • [3] Khatibi, R., Naghipour, L., Ghorbani, M. A., Smith, M. S., Karimi, V., Farhoudi, R., Delafrouz, H., Arvanaghi, H. (2013). Developing a predictive tropospheric ozone model for Tabriz. Tabriz Atmospheric Environment, 68, 286–293.
  • [4] Tiku, M. L. (1967a). Estimating the mean and standard deviation from a censored normal sample. Biometrika, 54, 155-165.
  • [5] Tiku, M. L. (1967b). A note on estimating the location and scale parameters of the exponential distribution from a censored sample. Australian Journal of Statistics, 9, 49-53.
  • [6] Tiku, M. L. (1968a). Estimating the parameters of log-normal distribution from censored samples. Journal of American Statistical Association, 63, 134-140.
  • [7] Tiku, M. L. (1968b). Estimating the parameters of normal and logistic distributions from censored samples. Australian Journal of Statistics, 10, 64-73.
  • [8] Tiku, M. L. (1968c) "Estimating the mean and standard deviation from progressively censored normal samples" J. Ind. Soc. Agric. Stat., 20, 20-25.
  • [9] Tiku, M. L. (1970). Monte Carlo study of some simple estimators in censored normal samples. Biometrika, 57, 207-210.
  • [10] Akkaya, A., Tiku, M.L. (2004). Robust Estimation and Hypothesis Testing. New Age International (P) Ltd, New Delhi.
  • [11] Trawinski, B.J., Bechhofer, R.E., Tamhane, A.C., Tiku, M.L., Kumra, S. (1985). Selected tables in mathematical statistics. American Mathematical Society.
  • [12] Rowland, J.H., Varol, Y.L. (1972). Exit criteria for Simpson’s compound rule. Math Computation, 26, 119, 699-702.
  • [13] Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (İkinci Baskı), Prentice-Hall, New Jersey.

ROBUST ARIMA MODELİ İLE YAPAY SİNİR AĞLARI MODELİNİN KIYASLANMASI: TURİZM ÖRNEĞİ

Year 2018, Issue: 040, 7 - 12, 15.06.2018

Abstract

Yapay sinir ağları yöntemi 20. yüzyılın ilk yarısından sonra popülerleşmiş bir yeni nesil analiz yöntemidir. Örüntü sınıflandırma, ses tanımlama vs. gibi alanlarda başarılı uygulamaları olan yapay sinir ağları yöntemi zaman serileri analizinde de son yıllarda çok sıklıkla uygulanmaya başlanmıştır. Ancak bahsi geçen alanlardan farklı olarak zaman serileri analizinde başarıya etki eden etkenler çok farklıdır ve zaman serisi modellerindeki parametrelerin tahmin edilmesi, seride eğer varsa kırılma noktalar vs. öngörüleri etkilemektedir. Bu nedenle, yapay sinir ağları yönteminin zaman serileri analizinde başarı sağlaması çok zor olmaktadır. Bu sebeple yapay sinir ağında performansı geliştirecek yeni bir yöntem önererek öngörü yeteneği bu çalışmada geliştirilmiştir. Robust teknikler en küçük verilerde bile işe yarayacak etkili zaman serisi analizi prosedürleri geliştirmişlerdir. Bunlardan etkili olarak kabul edebileceğimiz bir yöntem, modifiye en çok olabilirlik yöntemidir. Bu yöntemde herhangi bir zaman serisi modeli üzerinden elde edilecek modifiye en çok olabilirlik yönteminden elde edilen parametre tahminleri ile öngörü yapılabilmekte ve hatalarının dağılımına göre etkin ve yansız tahmin ediciler elde edilmektedir. Bu çalışmada turizm verisi üzerinde modifiye en çok olabilirlik yöntemi ile performansı geliştirilmiş yapay sinir ağının kıyaslaması yapılacaktır.

References

  • [1] Azadeh, A., Neshat, N., Mardan, E., Saberi, M. (2013). Optimization of steel demand forecasting with complete and uncertain economic inputs by an integrated neural network–fuzzy mathematical programming approach. International Journal of Advanced Manufactured Technology, 65, 833–841.
  • [2] Jhajharia, D., Chattopadhyay, S., Choudhary, R. R., Dev, Vas S., Vijay, P., Lal, S. (2013). Influence of climate on incidences of malaria in the Thar Desert for Northwest India. International Journal of Climatology, 33(2), 312-325.
  • [3] Khatibi, R., Naghipour, L., Ghorbani, M. A., Smith, M. S., Karimi, V., Farhoudi, R., Delafrouz, H., Arvanaghi, H. (2013). Developing a predictive tropospheric ozone model for Tabriz. Tabriz Atmospheric Environment, 68, 286–293.
  • [4] Tiku, M. L. (1967a). Estimating the mean and standard deviation from a censored normal sample. Biometrika, 54, 155-165.
  • [5] Tiku, M. L. (1967b). A note on estimating the location and scale parameters of the exponential distribution from a censored sample. Australian Journal of Statistics, 9, 49-53.
  • [6] Tiku, M. L. (1968a). Estimating the parameters of log-normal distribution from censored samples. Journal of American Statistical Association, 63, 134-140.
  • [7] Tiku, M. L. (1968b). Estimating the parameters of normal and logistic distributions from censored samples. Australian Journal of Statistics, 10, 64-73.
  • [8] Tiku, M. L. (1968c) "Estimating the mean and standard deviation from progressively censored normal samples" J. Ind. Soc. Agric. Stat., 20, 20-25.
  • [9] Tiku, M. L. (1970). Monte Carlo study of some simple estimators in censored normal samples. Biometrika, 57, 207-210.
  • [10] Akkaya, A., Tiku, M.L. (2004). Robust Estimation and Hypothesis Testing. New Age International (P) Ltd, New Delhi.
  • [11] Trawinski, B.J., Bechhofer, R.E., Tamhane, A.C., Tiku, M.L., Kumra, S. (1985). Selected tables in mathematical statistics. American Mathematical Society.
  • [12] Rowland, J.H., Varol, Y.L. (1972). Exit criteria for Simpson’s compound rule. Math Computation, 26, 119, 699-702.
  • [13] Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (İkinci Baskı), Prentice-Hall, New Jersey.
There are 13 citations in total.

Details

Primary Language Turkish
Subjects Mathematical Sciences
Journal Section Articles
Authors

Selim Dönmez This is me 0000-0003-0674-1830

Özer Özaydın 0000-0001-6657-1162

Publication Date June 15, 2018
Published in Issue Year 2018 Issue: 040

Cite

APA Dönmez, S., & Özaydın, Ö. (2018). ROBUST ARIMA MODELİ İLE YAPAY SİNİR AĞLARI MODELİNİN KIYASLANMASI: TURİZM ÖRNEĞİ. Journal of Science and Technology of Dumlupınar University(040), 7-12.

HAZİRAN 2020'den itibaren Journal of Scientific Reports-A adı altında ingilizce olarak yayın hayatına devam edecektir.