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Adaptive trees : a new approach to economic forecasting

Woloszko, Nicolas - 2020

The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the...

Persistent link: https://www.econbiz.de/10012203223

On multivariate randomized classification trees l0- based sparsity, VC dimension and decomposition methods

Amaldi, Edoardo; Consolo, Antonio; Manno, Andrea - In: Computers & operations research : and their … 151 (2023), pp. 1-21

Persistent link: https://www.econbiz.de/10014234818

Understanding machine learning-based forecasting methods : a decomposition framework and research opportunities

Bojer, Casper Solheim - In: International journal of forecasting 38 (2022) 4, pp. 1555-1561

Persistent link: https://www.econbiz.de/10014381162

Decomposition of inequality of opportunity in India : an application of data-driven machine learning approach

Mehta, Balwant Singh; Dhote, Siddharth; Srivastava, Ravi - In: The Indian journal of labour economics : a quarterly … 66 (2023) 2, pp. 439-469

Persistent link: https://www.econbiz.de/10014382657

Fine-grained tourism demand forecasting : a decomposition ensemble deep learning model

Bi, Jian-Wu; Han, Tian-Yu; Yao, Yanbo - In: Tourism economics : the business and finance of tourism … 29 (2023) 7, pp. 1736-1763

Persistent link: https://www.econbiz.de/10014384540

A machine learning based branch-cut-and-Benders for dock assignment and truck scheduling problem in cross-docks

Neamatian Monemi, Rahimeh; Gelareh, Shahin; Maculan F., … - In: Transportation research / E : an international journal 178 (2023), pp. 1-19

Persistent link: https://www.econbiz.de/10014437651

Use of machine learning models to warmstart column generation for unit commitment

Sugishita, Nagisa; Grothey, Andreas; McKinnon, Ken - In: INFORMS journal on computing : JOC ; charting new … 36 (2024) 4, pp. 1129-1146

Persistent link: https://www.econbiz.de/10015163759

Random sampling and machine learning to understand good decompositions

Basso, S.; Ceselli, A.; Tettamanzi, A. - In: Decomposition methods for hard optimization problems, (pp. 501-526). 2020

Persistent link: https://www.econbiz.de/10012157111

Decomposition methods for tourism demand forecasting : a comparative study

Zhang, Chengyuan; Li, Mingchen; Sun, Shaolong; Tang, Ling; … - In: Journal of travel research : a quarterly publication of … 61 (2022) 7, pp. 1682-1699

Persistent link: https://www.econbiz.de/10013390876

Intrinsic decompositions in gold forecasting

Plakandaras, Vasilios; Ji, Qiang - In: Journal of commodity markets 28 (2022), pp. 1-15

Persistent link: https://www.econbiz.de/10014335247