Forecasting with gradient boosted trees : augmentation, tuning, and cross-validation strategies : winning solution to the M5 uncertainty competition
A. David Lainder, Russell D. Wolfinger
| Year of publication: |
2022
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|---|---|
| Authors: | Lainder, A. David ; Wolfinger, Russell D. |
| Published in: |
International journal of forecasting. - Amsterdam [u.a.] : Elsevier, ISSN 0169-2070, ZDB-ID 283943-X. - Vol. 38.2022, 4, p. 1426-1433
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| Subject: | Feature engineering | Forecasting competitions | Gradient boosted trees | M competitions | Machine learning | Neural networks | Probabilistic forecasts | Purged k-fold cross-validation | Retail sales forecasting | Time series | Time-series forecasting | Uncertainty | Prognoseverfahren | Forecasting model | Zeitreihenanalyse | Time series analysis | Neuronale Netze | Theorie | Theory | Künstliche Intelligenz | Artificial intelligence | Prognose | Forecast | Wettbewerb | Competition |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.ijforecast.2021.12.003 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014381101