Forecasting with trees
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DOI: 10.1016/j.ijforecast.2021.10.004
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References listed on IDEAS
- Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
- Montero-Manso, Pablo & Hyndman, Rob J., 2021.
"Principles and algorithms for forecasting groups of time series: Locality and globality,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
- Pablo Montero-Manso & Rob J Hyndman, 2020. "Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality," Monash Econometrics and Business Statistics Working Papers 45/20, Monash University, Department of Econometrics and Business Statistics.
- Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020.
"FFORMA: Feature-based forecast model averaging,"
International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
- Pablo Montero-Manso & George Athanasopoulos & Rob J Hyndman & Thiyanga S Talagala, 2018. "FFORMA: Feature-based forecast model averaging," Monash Econometrics and Business Statistics Working Papers 19/18, Monash University, Department of Econometrics and Business Statistics.
- Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
- Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
- Tim Januschowski & Stephan Kolassa, 2019. "A Classification of Business Forecasting Problems," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 52, pages 36-43, Winter.
- Spyros Makridakis & Evangelos Spiliotis, 2021. "The M5 Competition and the Future of Human Expertise in Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 60, pages 33-37, Winter.
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Cited by:
- Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.
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Keywords
Random forests; Probabilistic forecasting; Gradient Boosted Trees; Global forecasting models; Deep Learning;All these keywords.
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