Forecasting with gradient boosted trees : augmentation, tuning, and cross-validation strategies : winning solution to the M5 uncertainty competition
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 |
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