Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pgph.0000495
Download full text from publisher
References listed on IDEAS
- Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Mizuho Nishio & Mitsuo Nishizawa & Osamu Sugiyama & Ryosuke Kojima & Masahiro Yakami & Tomohiro Kuroda & Kaori Togashi, 2018. "Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-13, April.
- Rosselló, Jaume & Sansó, Andreu, 2017. "Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis," Tourism Management, Elsevier, vol. 60(C), pages 379-389.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Mst Noorunnahar & Arman Hossain Chowdhury & Farhana Arefeen Mila, 2023. "A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
- Md Siddikur Rahman & Arman Hossain Chowdhury, 2022. "A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-14, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chandra, Aitichya & Verma, Ashish & Sooraj, K.P. & Padhi, Radhakant, 2023. "Modelling and assessment of the arrival and departure process at the terminal area: A case study of Chennai international airport," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
- Frédéric Dobruszkes & Jean-Michel Decroly & Pere Suau-Sanchez, 2022. "The monthly rhythms of aviation: A global analysis of passenger air service seasonality," ULB Institutional Repository 2013/341140, ULB -- Universite Libre de Bruxelles.
- Amir Khatibi & Ana Paula Couto da Silva & Jussara M Almeida & Marcos A Gonçalves, 2022. "A quantitative analysis of the impact of explicit incorporation of recency, seasonality and model specialization into fine-grained tourism demand prediction models," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-31, December.
- Boto-García, David & Pérez, Levi, 2023. "The effect of high-speed rail connectivity and accessibility on tourism seasonality," Journal of Transport Geography, Elsevier, vol. 107(C).
- Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
- Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
- Dimitrios TSIOTAS & Thomas KRABOKOUKIS & Serafeim POLYZOS, 2020. "Detecting Interregional Patterns In Tourism Seasonality Of Greece: A Principal Components Analysis Approach," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 91-112, June.
- Yongchao Jin & Renfang Wang & Xiaodie Zhuang & Kenan Wang & Honglian Wang & Chenxi Wang & Xiyin Wang, 2022. "Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model," Mathematics, MDPI, vol. 10(21), pages 1-13, October.
- Xuemin Huang & Xiaoliang Zhuang & Fangyuan Tian & Zheng Niu & Yujie Chen & Qian Zhou & Chao Yuan, 2025. "A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction," Energies, MDPI, vol. 18(6), pages 1-22, March.
- Thomas Krabokoukis & Serafeim Polyzos, 2024. "Analyzing the Tourism Seasonality for the Mediterranean Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 8053-8076, June.
- Alessandro V. M. Oliveira, 2024. "The seasonality of air ticket prices before and after the pandemic," Papers 2402.13789, arXiv.org.
- Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder, 2025. "Development of multi-forecasting model using Monte Carlo simulation coupled with wavelet denoising-ARIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 517-540.
- Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
- Hirad Baradaran Rezaei & Alireza Amjadian & Mohammad Vahid Sebt & Reza Askari & Abolfazl Gharaei, 2023. "An ensemble method of the machine learning to prognosticate the gastric cancer," Annals of Operations Research, Springer, vol. 328(1), pages 151-192, September.
- Md Siddikur Rahman & Arman Hossain Chowdhury, 2022. "A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-14, September.
- Haithem Awijen & Hachmi Ben Ameur & Zied Ftiti & Waël Louhichi, 2025. "Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models," Annals of Operations Research, Springer, vol. 345(2), pages 979-1002, February.
- Zou, Li & Reynolds-Feighan, Aisling & Yu, Chunyan, 2022. "Airline seasonality: An explorative analysis of major low-cost carriers in Europe and the United States," Journal of Air Transport Management, Elsevier, vol. 105(C).
- Daren Zhao & Huiwu Zhang & Qing Cao & Zhiyi Wang & Sizhang He & Minghua Zhou & Ruihua Zhang, 2022. "The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pgph00:0000495. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: globalpubhealth (email available below). General contact details of provider: https://journals.plos.org/globalpublichealth .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.