Araştırma Makalesi
BibTex RIS Kaynak Göster

An Approach for Airfare Prices Analysis with Penalized Regression Methods

Yıl 2021, Cilt: 4 Sayı: 2, 57 - 61, 19.08.2021

Öz

At present, the number of passengers preferring to use the airline is increasing with each passing day. Thus, correctly analysing the airfare prices is essential to raise awareness of passengers. Some researchers have applied different kinds of Machine Learning (ML) algorithms to predict the airfare prices. However, to the best of our knowledge, penalized regression methods have not been used to analyse the airfare prices. Ridge, Lasso and Elastic Net regressions are penalized regression methods. The dataset used in this study consists of 1814 one-way flights from Greece to Germany. The developed Ridge, Lasso and Elastic Net methods were achieved to provide convincing results (MSE) for airfare prices analysis (Ridge:160103, Lasso:159280, Elastic Net:174203). The results and findings reveal that the proposed Lasso method is potentially better than the others in the analysis of datasets consisting one-way of flights.

Kaynakça

  • [1] Abdella, J. A., Zaki, N., Shuaib, K., & Khan, F.. “Airline ticket price and demand prediction: A survey.” Journal of King Saud University-Computer and Information Sciences. (2019)
  • [2] Szabo, S., Mako, S., Tobisova, A., Hanak, P., & Pilat, M. “Effect of the load factor on the ticket price”. Transport problems, 13. (2018).
  • [3] Groves, W., & Gini, M. “A regression model for predicting optimal purchase timing for airline tickets,” Technical Report 11-025, University of Minnesota, Minneapolis, 2011.
  • [4] Groves, W., & Gini, M. “An agent for optimizing airline ticket purchasing,” 12th International Conference on Autonomous Agents and Multiagent Systems, St. Paul, MN, pp. 1341-1342, May 06 - 10, 2013.
  • [5] Tziridis, K., Kalampokas, T., Papakostas, G. A., & Diamantaras, K. I. Airfare prices prediction using machine learning techniques. 25th European Signal Processing Conference (EUSIPCO), IEEE, 1036-1039, 2017.
  • [6] Ajana, S., Acar, N., Bretillon, L., Hejblum, B. P., Jacqmin-Gadda, H., & Delcourt, C. “Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size”. Bioinformatics, 35(19), 3628-3634, 2019.
  • [7] James, G., Witten, D., Hastie, T., & Tibshirani, R. “An introduction to statistical learning”, Vol. 112, p. 18,. New York: springer, 2013.
  • [8] Wang, T., Pouyanfar, S., Tian, H., Tao, Y., Alonso, M., Luis, S., & Chen, S. C.. “A framework for airfare price prediction: A machine learning approach”. 20th International Conference on Information Reuse and Integration for Data Science (IRI), IEEE, pp. 200-207, 2019.
  • [9] Lu, J. “Machine learning modeling for time series problem: Predicting flight ticket prices”. arXiv preprint arXiv:1705.07205, 2017.
  • [10] Ren, Q. When to Book: Predicting Flight Pricing. Standford university.
  • [11] Groves, W., & Gini, M. “An agent for optimizing airline ticket purchasing”. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, 1341-1342, 2013
  • [12] M. Papadakis, “Predicting Airfare Prices,” 2012.
  • [13] https://github.com/humain-lab/airfare_prediction. (14.04.2021).
  • [14] Uzut O.G., Buyrukoglu S. “Prediction of real estate prices with data mining algorithms”. Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences. 2020;77-84. 2020, https://doi.org/10.38065/euroasiaorg.81
  • [15] Zhang, Z., Lai, Z., Xu, Y., Shao, L., Wu, J., & Xie, G. S. “Discriminative elastic-net regularized linear regression”. IEEE Transactions on Image Processing, 26(3), 1466-1481, 2017.
  • [16] Muniz, G., & Kibria, B. G. “On some ridge regression estimators: An empirical comparisons”. Communications in Statistics—Simulation and Computation, 38(3), 621-630, 2009.
  • [17] Fushiki, T. (2011). “Estimation of prediction error by using K-fold cross-validation”. Statistics and Computing, 21(2), 137-146, 2011

An Approach for Airfare Prices Analysis with Penalized Regression Methods

Yıl 2021, Cilt: 4 Sayı: 2, 57 - 61, 19.08.2021

Öz

Günümüzde havayolu kullanmayı tercih eden yolcu sayısı her geçen gün artmaktadır. Bu nedenle uçak bileti fiyatlarının doğru analiz edilmesi yolcuların bilinçlendirilmesi açısından önemlidir. Bazı araştırmacılar, uçak bileti fiyatlarını analiz etmek için farklı türden Makine Öğrenimi (ML) modelleri uyguladılar. Ancak, bildiğimiz kadarıyla, uçak bileti fiyatlarını analiz etmek için cezalı regresyon yöntemleri uygulanmadı. Ridge, Lasso ve Elastic Net regresyonları cezalandırılmış regresyon yöntemleridir. Bu çalışmada kullanılan veri seti Yunanistan'dan Almanya'ya 1814 tek yönlü uçuştan oluşmaktadır. Geliştirilen Ridge, Lasso ve Elastic Net yöntemleri, uçak bileti fiyatları analizi (Ridge:160103, Lasso:159280, Elastic Net:174203) için ikna edici sonuçlar (MSE) elde etmiştir. Sonuçlar ve bulgular, önerilen Lasso yönteminin tek yönlü uçuşlardan oluşan veri setlerinin analizinde potansiyel olarak diğerlerinden daha iyi olduğunu ortaya koymaktadır.

Kaynakça

  • [1] Abdella, J. A., Zaki, N., Shuaib, K., & Khan, F.. “Airline ticket price and demand prediction: A survey.” Journal of King Saud University-Computer and Information Sciences. (2019)
  • [2] Szabo, S., Mako, S., Tobisova, A., Hanak, P., & Pilat, M. “Effect of the load factor on the ticket price”. Transport problems, 13. (2018).
  • [3] Groves, W., & Gini, M. “A regression model for predicting optimal purchase timing for airline tickets,” Technical Report 11-025, University of Minnesota, Minneapolis, 2011.
  • [4] Groves, W., & Gini, M. “An agent for optimizing airline ticket purchasing,” 12th International Conference on Autonomous Agents and Multiagent Systems, St. Paul, MN, pp. 1341-1342, May 06 - 10, 2013.
  • [5] Tziridis, K., Kalampokas, T., Papakostas, G. A., & Diamantaras, K. I. Airfare prices prediction using machine learning techniques. 25th European Signal Processing Conference (EUSIPCO), IEEE, 1036-1039, 2017.
  • [6] Ajana, S., Acar, N., Bretillon, L., Hejblum, B. P., Jacqmin-Gadda, H., & Delcourt, C. “Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size”. Bioinformatics, 35(19), 3628-3634, 2019.
  • [7] James, G., Witten, D., Hastie, T., & Tibshirani, R. “An introduction to statistical learning”, Vol. 112, p. 18,. New York: springer, 2013.
  • [8] Wang, T., Pouyanfar, S., Tian, H., Tao, Y., Alonso, M., Luis, S., & Chen, S. C.. “A framework for airfare price prediction: A machine learning approach”. 20th International Conference on Information Reuse and Integration for Data Science (IRI), IEEE, pp. 200-207, 2019.
  • [9] Lu, J. “Machine learning modeling for time series problem: Predicting flight ticket prices”. arXiv preprint arXiv:1705.07205, 2017.
  • [10] Ren, Q. When to Book: Predicting Flight Pricing. Standford university.
  • [11] Groves, W., & Gini, M. “An agent for optimizing airline ticket purchasing”. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, 1341-1342, 2013
  • [12] M. Papadakis, “Predicting Airfare Prices,” 2012.
  • [13] https://github.com/humain-lab/airfare_prediction. (14.04.2021).
  • [14] Uzut O.G., Buyrukoglu S. “Prediction of real estate prices with data mining algorithms”. Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences. 2020;77-84. 2020, https://doi.org/10.38065/euroasiaorg.81
  • [15] Zhang, Z., Lai, Z., Xu, Y., Shao, L., Wu, J., & Xie, G. S. “Discriminative elastic-net regularized linear regression”. IEEE Transactions on Image Processing, 26(3), 1466-1481, 2017.
  • [16] Muniz, G., & Kibria, B. G. “On some ridge regression estimators: An empirical comparisons”. Communications in Statistics—Simulation and Computation, 38(3), 621-630, 2009.
  • [17] Fushiki, T. (2011). “Estimation of prediction error by using K-fold cross-validation”. Statistics and Computing, 21(2), 137-146, 2011
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Selim Buyrukoğlu

Yıldıran Yılmaz

Yayımlanma Tarihi 19 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA Buyrukoğlu, S., & Yılmaz, Y. (2021). An Approach for Airfare Prices Analysis with Penalized Regression Methods. Veri Bilimi, 4(2), 57-61.



Dergimizin Tarandığı Dizinler (İndeksler)


Academic Resource Index

logo.png

journalseeker.researchbib.com

Google Scholar

scholar_logo_64dp.png

ASOS Index

asos-index.png

Rooting Index

logo.png

www.rootindexing.com

The JournalTOCs Index

journal-tocs-logo.jpg?w=584

www.journaltocs.ac.uk

General Impact Factor (GIF) Index

images?q=tbn%3AANd9GcQ0CrEQm4bHBnwh4XJv9I3ZCdHgQarj_qLyPTkGpeoRRmNh10eC

generalif.com

Directory of Research Journals Indexing

DRJI_Logo.jpg

olddrji.lbp.world/indexedJournals.aspx

I2OR Index

8c492a0a466f9b2cd59ec89595639a5c?AccessKeyId=245B99561176BAE11FEB&disposition=0&alloworigin=1

http://www.i2or.com/8.html



logo.png