Comparative Analysis of ARIMA and Machine Learning Models for Uncertainty Quantification in Hierarchical Welfare Volume Forecasting

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Keywords	Prediction intervals, Machine learning, ARIMA, Hierarchical forecasting, Uncertainty Quantification, Calibration, Sharpness
Thesis Advisor	Hurovich, Gustavo, Tekin, Müge
Persistent URL	hdl.handle.net/2105/77338
Series	Business Analytics & Management
Organisation	Rotterdam School of Management
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Türkmen, Bersu. (2025). Comparative Analysis of ARIMA and Machine Learning Models for Uncertainty Quantification in Hierarchical Welfare Volume Forecasting. In Business Analytics & Management.http://hdl.handle.net/2105/77338

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