Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies

A. David Lainder and Russell D. Wolfinger

International Journal of Forecasting, 2022, vol. 38, issue 4, 1426-1433

Abstract: Deep neural networks and gradient boosted tree models have swept across the field of machine learning over the past decade, producing across-the-board advances in performance. The ability of these methods to capture feature interactions and nonlinearities makes them exceptionally powerful and, at the same time, prone to overfitting, leakage, and a lack of generalization in domains with target non-stationarity and collinearity, such as time-series forecasting. We offer guidance to address these difficulties and provide a framework that maximizes the chances of predictions that generalize well and deliver state-of-the-art performance. The techniques we offer for cross-validation, augmentation, and parameter tuning have been used to win several major time-series forecasting competitions—including the M5 Forecasting Uncertainty competition and the Kaggle COVID19 Forecasting series—and, with the proper theoretical grounding, constitute the current best practices in time-series forecasting.

Keywords: Gradient boosted trees; Neural networks; Purged k-fold cross-validation; Feature engineering; Forecasting competitions; M competitions; Uncertainty; Probabilistic forecasts; Time series; Machine learning; Retail sales forecasting; Time-series forecasting (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207021002090
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1426-1433

DOI: 10.1016/j.ijforecast.2021.12.003

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().