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TÜRKİYE'DE DOĞRULANMIŞ COVİD-19 VAKALARININ İSTİFLEME TOPLULUK MODELLER İLE ZAMAN SERİSİ TAHMİNİ

Year 2023, Issue: 26, 504 - 520, 28.10.2023

Cihan Çılgın , Mehmet Ozan Özdemir

https://doi.org/10.29029/busbed.1299248

Abstract

COVID-19 hemen hemen her ülkeye yayıldığı ve insanlık için ciddi bir tehdit oluşturduğu için DSÖ tarafından pandemi olarak ilan edilmiştir. Bir pandeminin sonuçlarını tahmin etmek, politika yapıcılar ve karar vericiler için oldukça önemli ve zor bir görevdir. Bu çalışmanın amacı, çeşitli zaman serisi modelleme yaklaşımlarını kullanarak Türkiye'deki günlük vaka sayılarını tahmin etmektir. Bu kapsamda 11 Mart 2020 ile 24 Aralık 2021 tarihleri arasındaki pozitif vaka sayıları bu çalışmada dikkate alınmıştır. Bu çalışma, kapsadığı gözlem sayısı ile daha az gözlem sayısı ile yapılmış diğer çalışmalardan ayrılmaktadır. Bu çalışmada COVID 19 pandemisi sırasındaki tüm dalgalar daha geniş bir zaman diliminde incelenerek analize dâhil edilmiştir. Ayrıca çalışmamızda makine öğrenmesi algoritmalarının bu algoritmalar ile durum tahmini yapılarak karşılaştırılması yanında, yığınlama yaklaşımı ile kullanılan tüm modellerin tahminleri tek bir model altında birleştirilerek tahmin performansının artırılması hedeflenmiştir. Çalışmamız, incelenen ilgili tüm çalışmaları dikkate alarak, bildiğimiz kadarıyla, bu kadar çok model performansını bir arada değerlendiren ve COVID-19 vaka sayıları üzerinde bir yığınlama modeli oluşturan ilk çalışmadır. Çalışmadan elde edilen bulgular, geliştirilen istifleme modeli ile doğrulanan durum tahminlerinin yüksek doğrulukta yapıldığını ve tüm toplu öğrenme yaklaşımlarının bireysel yöntemlere göre daha iyi sonuçlar verdiğini kanıtlamaktadır.

Keywords

COVID-19 zaman serisi tahmin makine öğrenimi coronavirüs genelleştirilmiş yığınlama modelleri

References

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TIME SERIES FORECASTING OF COVID-19 CONFIRMED CASES IN TURKEY WITH STACKING ENSEMBLE MODELS

Year 2023, Issue: 26, 504 - 520, 28.10.2023

Cihan Çılgın , Mehmet Ozan Özdemir

https://doi.org/10.29029/busbed.1299248

Abstract

Since COVID-19 has spread almost across any country and is a serious threat to mankind, it was declared to be a pandemic by WHO. Forecasting the results of a pandemic is a quite important and difficult task for policy makers and decision makers. The aim of this study is to forecast the daily case numbers in Turkey by using various time series modeling approaches. In this context, positive case numbers between March 11, 2020, and December 24, 2021, were taken into account in this study. This study, with the number of observations it covers, differentiates from other studies which have been conducted with few number of observations. In this study, all the waves during the COVID 19 pandemic were included in the analysis by studying a more extensive time period. Moreover, in our study, along with a comparison of machine learning algorithms by making case forecasting with these algorithms, increasing the forecasting performance was aimed by combining the predictions of all models used with the stacking approach under a single model. By taking all the related studies analyzed into account, our study, as far as we know, is the first one to assess this many model performances together and make a stacking model on COVID-19 case numbers. The findings obtained from the study prove that forecasting of the cases validated via the developed stacking model were made with high accuracy, and all ensemble learning approaches produce better results than individual methods.

Keywords

COVID-19 time-series forecasting machine learning coronavirus generalized stacking models

References

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  • Couronné, R., Probst, P., & Boulesteix, A. L. (2018). Random forest versus logistic regression: A large-scale benchmark experiment. BMC bioinformatics, 19(1), 1-14. https://doi.org/10.1186/s12859-018-2264-5.
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Details

Primary Language English
Subjects Management Information Systems
Journal Section Articles
Authors

Cihan Çılgın 0000-0002-8983-118X

Mehmet Ozan Özdemir 0000-0002-4224-1190

Early Pub Date October 27, 2023
Publication Date October 28, 2023
Published in Issue Year 2023Issue: 26

Cite

APA Çılgın, C., & Özdemir, M. O. (2023). TIME SERIES FORECASTING OF COVID-19 CONFIRMED CASES IN TURKEY WITH STACKING ENSEMBLE MODELS. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(26), 504-520. https://doi.org/10.29029/busbed.1299248
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