Research Article
BibTex RIS Cite

Year 2020, Volume: 35 Issue: 1, 467 - 478, 25.10.2019

Ümit Çavuş Büyükşahin Şeyda Ertekin

https://doi.org/10.17341/gazimmfd.508394
Cited By: 1

Abstract

References

  • Weigend, A, Time series prediction: forecasting the future and understanding the past, New York: Routledge, 2018.
  • J. Contreras, R. Espinola, F. Nogales, A. J. Conejo, ARIMA Models to Predict Next-Day Electricity Prices, Power Engineering Review, IEEE 22 (2002) 57-57. doi:10.1109/MPER.2002.4312577.
  • E. Gonzalez-Romera, M. A. Jaramillo-Moran, D. Carmona-Fernandez, Monthly Electric Energy Demand Forecasting Based on Trend Extraction, IEEE Transactions on Power Systems 21 (4) (2006) 1946-1953. doi:10.1109/TPWRS.2006.883666.
  • V. K P, P. Sahu, B. Dhekale, P. Mishra, Modelling and Forecasting Sugarcane and Sugar Production in India, Indian Journal of Economics and Development 12 (2016) 71. doi:10.5958/2322-0430.2016.00009.3.
  • W.-c. Wang, K.-w. Chau, D.-M. Xu, X.-Y. Chen, Improving Forecasting Accuracy of Annual Runo Time Series Using ARIMA Based on EEMD Decomposition, Water Resources Management 29 (2015) 2655-2675. doi: 10.1007/s11269-015-0962-6.
  • V. Ediger, S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy 35 (2007) 1701-1708. doi:10.1016/j.enpol.2006.05.009.
  • A. Lapedes, R. Farber, Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling, 1. IEEE international conference on neural networks, 1987, San Diego.
  • W.-S. Chen, Y.-K. Du, Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model, Expert Syst. Appl. 36 (2) (2009) 4075-4086. doi:10.1016/j.eswa.2008.03.020.
  • D. Singhal, K. S. Swarup, Electricity price forecasting using artificial neural networks, International Journal of Electrical Power Energy Systems 33 (3) (2011) 550-555. doi:10.1016/j.ijepes.2010.12.009.
  • C. H. F. Toro, S. Gomez Meire, J. F. Galvez, F. Fdez-Riverola, A hybrid artificial intelligence model for river flow forecasting, Applied Soft Computing 13 (8) (2013) 3449-3458. doi:10.1016/j.asoc.2013.04.014.
  • B. R. Chang, H. F. Tsai, Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis, Applied Soft Computing 9 (3) (2009) 1177-1183. doi:10.1016/j.asoc.2009.03.003.
  • G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems (4) (1992) 455-455. doi:10.1007/BF02134016.
  • K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (5) (1989) 359-366. doi: 10.1016/0893-6080(89)90020-8.
  • P. Zhang, Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159-175, Neurocomputing 50 (2003) 159-175. doi:10.1016/S0925-2312(01)00702-0.
  • W. R. Foster, F. Collopy, L. H. Ungar, Neural network forecasting of short, noisy time series, Computers Chemical Engineering 16 (4) (1992) 293-297. doi:10.1016/0098-1354(92)80049-F.
  • S. Aras, D. Kocako, A new model selection strategy in time series forecasting with artificial neural networks: IHTS, Neurocomputing 174 (2016) 974-987. doi:10.1016/j.neucom.2015.10.036.
  • P. Chakradhara and V. Narasimhan, Forecasting exchange rate better with artificial neural network, Journal of Policy Modeling 29, pp. 227–236, 2007.
  • M. Khashei, M. Bijari, A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting, Appl. Soft Comput. 11 (2) (2011) 2664-2675. doi:10.1016/j.asoc.2010.10.015.
  • C. N. Babu, B. E. Reddy, A Moving-average Filter Based Hybrid ARIMA-ANN Model for Forecasting Time Series Data, Appl. Soft Comput. 23 (2014) 27-38. doi:10.1016/j.asoc.2014.05.028.
  • Ü. Ç. Büyükşahin, Ş. Ertekin, Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition, arXiv:1812.11526 [cs], Jan 2019.
  • X. Wang, K. Smith, R. Hyndman, Characteristic-based clustering for time series data, Data Min. Knowl. Discov. 13 (3) (2006) 335–364.
  • X. Wang, K. Smith, R. Hyndman. (2005) Dimension Reduction for Clustering Time Series Using Global Characteristics. In: Sunderam V.S., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3516. Springer, Berlin, Heidelberg
  • B. D. Fulcher and N. S. Jones. Highly Comparative Feature-Based Time-Series Classification. In: IEEE Transactions on Knowledge and Data Engineering 26.12 (Dec. 2014), pp. 3026–3037. issn: 1041-4347. doi: 10.1109/TKDE.2014.2316504.
  • RJ Hyndman, E Wang, N Laptev, Large-scale unusual time series detection. IEEE International Conference on Data Mining Workshop (ICDMW), 14-17 Nov. 2015. doi: 10.1109/ICDMW.2015.104.
  • N. Laptev, J. Yosinski, L. Erran, S. Smyl, Time-series Extreme Event Forecasting with Neural Networks at Uber, International Conference on Machine Learning, 2017.
  • E. T. Hibon M., To combine or not to combine: selecting among forecasts and their combinations. International Journal of Forecasting, International Journal of Forecasting 21 (2005) 15-24.
  • Friedman, J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38, 367–378.
  • Friedman, J.; Hastie, T.; Tibshirani, R. The Elements of Statistical Learning; Springer Series in Statistics; Springer: Berlin, Germany, 2001; Volume 1.
  • Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.I. (1984). Classification and regression trees. Belmont, Calif.: Wadsworth.
  • Intraday EPIAS, https://www.epias.com.tr/en/intra-day-market/ introduction, 2018, (Erişim tarihi Kasım 12, 2018).

Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli

Year 2020, Volume: 35 Issue: 1, 467 - 478, 25.10.2019

Ümit Çavuş Büyükşahin Şeyda Ertekin

https://doi.org/10.17341/gazimmfd.508394
Cited By: 1

Abstract

Zaman serilerinde yüksek performanslı tahminleme
yapabilmek birçok uygulama alanı için temel öneme sahiptir.  Literatürde zaman serisi tahmin doğruluğunu
artırmak için birçok metot önerilmiştir. Bu metotlardan tek değişkenli zaman
serilerine odaklanmış olanlar, serinin sadece geçmiş tarihinde yer alan
değerleri kullanarak, gelecekteki değerlerin tahminini yapmaktadır. Bu
çalışmada, tek değişkenli zaman serilerinin geçmiş değerlerinin yanında,
serinin karakteristiğini özetleyen yapısal özniteliklerinin de kullanılarak,
tahmin performansının artırılması hedeflenmiştir. Zaman serilerinin
istatistiksel özetini sunan özniteliklerin önem puanları gradyan artırım
ağaçları (GBT) ile belirlenmektedir. En yüksek önem puanına sahip olan
öznitelikler, hibrit ARIMA-YSA modeline açıklayıcı ilave değişkenler olarak
verilmektedir. Geliştirilen yöntemin değerlendirilmesi dört farklı veri seti
üzerinde yapılmış olup, mevcut kabul görmüş yöntemlere kıyasla daha başarılı
sonuçlar elde edildiği görülmüştür.

Keywords

Zaman serisi tahmini yapay sinir ağı otoregresif bütünleşik hareketli ortalama

References

  • Weigend, A, Time series prediction: forecasting the future and understanding the past, New York: Routledge, 2018.
  • J. Contreras, R. Espinola, F. Nogales, A. J. Conejo, ARIMA Models to Predict Next-Day Electricity Prices, Power Engineering Review, IEEE 22 (2002) 57-57. doi:10.1109/MPER.2002.4312577.
  • E. Gonzalez-Romera, M. A. Jaramillo-Moran, D. Carmona-Fernandez, Monthly Electric Energy Demand Forecasting Based on Trend Extraction, IEEE Transactions on Power Systems 21 (4) (2006) 1946-1953. doi:10.1109/TPWRS.2006.883666.
  • V. K P, P. Sahu, B. Dhekale, P. Mishra, Modelling and Forecasting Sugarcane and Sugar Production in India, Indian Journal of Economics and Development 12 (2016) 71. doi:10.5958/2322-0430.2016.00009.3.
  • W.-c. Wang, K.-w. Chau, D.-M. Xu, X.-Y. Chen, Improving Forecasting Accuracy of Annual Runo Time Series Using ARIMA Based on EEMD Decomposition, Water Resources Management 29 (2015) 2655-2675. doi: 10.1007/s11269-015-0962-6.
  • V. Ediger, S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy 35 (2007) 1701-1708. doi:10.1016/j.enpol.2006.05.009.
  • A. Lapedes, R. Farber, Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling, 1. IEEE international conference on neural networks, 1987, San Diego.
  • W.-S. Chen, Y.-K. Du, Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model, Expert Syst. Appl. 36 (2) (2009) 4075-4086. doi:10.1016/j.eswa.2008.03.020.
  • D. Singhal, K. S. Swarup, Electricity price forecasting using artificial neural networks, International Journal of Electrical Power Energy Systems 33 (3) (2011) 550-555. doi:10.1016/j.ijepes.2010.12.009.
  • C. H. F. Toro, S. Gomez Meire, J. F. Galvez, F. Fdez-Riverola, A hybrid artificial intelligence model for river flow forecasting, Applied Soft Computing 13 (8) (2013) 3449-3458. doi:10.1016/j.asoc.2013.04.014.
  • B. R. Chang, H. F. Tsai, Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis, Applied Soft Computing 9 (3) (2009) 1177-1183. doi:10.1016/j.asoc.2009.03.003.
  • G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems (4) (1992) 455-455. doi:10.1007/BF02134016.
  • K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (5) (1989) 359-366. doi: 10.1016/0893-6080(89)90020-8.
  • P. Zhang, Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159-175, Neurocomputing 50 (2003) 159-175. doi:10.1016/S0925-2312(01)00702-0.
  • W. R. Foster, F. Collopy, L. H. Ungar, Neural network forecasting of short, noisy time series, Computers Chemical Engineering 16 (4) (1992) 293-297. doi:10.1016/0098-1354(92)80049-F.
  • S. Aras, D. Kocako, A new model selection strategy in time series forecasting with artificial neural networks: IHTS, Neurocomputing 174 (2016) 974-987. doi:10.1016/j.neucom.2015.10.036.
  • P. Chakradhara and V. Narasimhan, Forecasting exchange rate better with artificial neural network, Journal of Policy Modeling 29, pp. 227–236, 2007.
  • M. Khashei, M. Bijari, A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting, Appl. Soft Comput. 11 (2) (2011) 2664-2675. doi:10.1016/j.asoc.2010.10.015.
  • C. N. Babu, B. E. Reddy, A Moving-average Filter Based Hybrid ARIMA-ANN Model for Forecasting Time Series Data, Appl. Soft Comput. 23 (2014) 27-38. doi:10.1016/j.asoc.2014.05.028.
  • Ü. Ç. Büyükşahin, Ş. Ertekin, Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition, arXiv:1812.11526 [cs], Jan 2019.
  • X. Wang, K. Smith, R. Hyndman, Characteristic-based clustering for time series data, Data Min. Knowl. Discov. 13 (3) (2006) 335–364.
  • X. Wang, K. Smith, R. Hyndman. (2005) Dimension Reduction for Clustering Time Series Using Global Characteristics. In: Sunderam V.S., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3516. Springer, Berlin, Heidelberg
  • B. D. Fulcher and N. S. Jones. Highly Comparative Feature-Based Time-Series Classification. In: IEEE Transactions on Knowledge and Data Engineering 26.12 (Dec. 2014), pp. 3026–3037. issn: 1041-4347. doi: 10.1109/TKDE.2014.2316504.
  • RJ Hyndman, E Wang, N Laptev, Large-scale unusual time series detection. IEEE International Conference on Data Mining Workshop (ICDMW), 14-17 Nov. 2015. doi: 10.1109/ICDMW.2015.104.
  • N. Laptev, J. Yosinski, L. Erran, S. Smyl, Time-series Extreme Event Forecasting with Neural Networks at Uber, International Conference on Machine Learning, 2017.
  • E. T. Hibon M., To combine or not to combine: selecting among forecasts and their combinations. International Journal of Forecasting, International Journal of Forecasting 21 (2005) 15-24.
  • Friedman, J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38, 367–378.
  • Friedman, J.; Hastie, T.; Tibshirani, R. The Elements of Statistical Learning; Springer Series in Statistics; Springer: Berlin, Germany, 2001; Volume 1.
  • Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.I. (1984). Classification and regression trees. Belmont, Calif.: Wadsworth.
  • Intraday EPIAS, https://www.epias.com.tr/en/intra-day-market/ introduction, 2018, (Erişim tarihi Kasım 12, 2018).
There are 30 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Ümit Çavuş Büyükşahin 0000-0002-6073-1695

Şeyda Ertekin This is me 0000-0002-6132-6739

Publication Date October 25, 2019
Submission Date January 4, 2019
Acceptance Date August 17, 2019
Published in Issue Year 2020 Volume: 35 Issue: 1

Cite

APA Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(1), 467-478. https://doi.org/10.17341/gazimmfd.508394
AMA Büyükşahin ÜÇ, Ertekin Ş. Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli. GUMMFD. October 2019;35(1):467-478. doi:10.17341/gazimmfd.508394
Chicago Büyükşahin, Ümit Çavuş, and Şeyda Ertekin. “Tek değişkenli Zaman Serileri Tahmini için öznitelik Tabanlı Hibrit ARIMA-YSA Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35, no. 1 (October 2019): 467-78. https://doi.org/10.17341/gazimmfd.508394.
EndNote Büyükşahin ÜÇ, Ertekin Ş (October 1, 2019) Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35 1 467–478.
IEEE Ü. Ç. Büyükşahin and Ş. Ertekin, “Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli”, GUMMFD, vol. 35, no. 1, pp. 467–478, 2019, doi: 10.17341/gazimmfd.508394.
ISNAD Büyükşahin, Ümit Çavuş - Ertekin, Şeyda. “Tek değişkenli Zaman Serileri Tahmini için öznitelik Tabanlı Hibrit ARIMA-YSA Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35/1 (October 2019), 467-478. https://doi.org/10.17341/gazimmfd.508394.
JAMA Büyükşahin ÜÇ, Ertekin Ş. Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli. GUMMFD. 2019;35:467–478.
MLA Büyükşahin, Ümit Çavuş and Şeyda Ertekin. “Tek değişkenli Zaman Serileri Tahmini için öznitelik Tabanlı Hibrit ARIMA-YSA Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 35, no. 1, 2019, pp. 467-78, doi:10.17341/gazimmfd.508394.
Vancouver Büyükşahin ÜÇ, Ertekin Ş. Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli. GUMMFD. 2019;35(1):467-78.

Cited By

Türkiye’de Devletin Tarımda 100 Yıllık Örgütleniş Süreci ve Yakın Geleceğe İlişkin Bir Değerlendirme
Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
https://doi.org/10.26745/ahbvuibfd.1364546