Forecast combinations: An over 50-year review
By Xiaoqian Wang, Rob J Hyndman, Feng Li, Yanfei Kang. International Journal of Forecasting, 2022
258 citations recorded by the source corpus.
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combining multiple forecasts produced from single (target) series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby mitigating the risk of identifying a single"best"forecast. Combination schemes have evolved from simple combination methods without estimation, to sophisticated methods involving time-varying weights, nonlinear combinations, correlations among components, and cross-learning. They include combining point forecasts and combining probabilistic forecasts. This paper provides an up-to-date review of the extensive literature on forecast combinations, together with reference to available open-source software implementations. We discuss the potential and limitations of various methods and highlight how these ideas have developed over time. Some important issues concerning the utility of forecast combinations are also surveyed. Finally, we conclude with current research gaps and potential insights for future research.
Subfields: Environmental Engineering, Global and Planetary Change, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/1bd79b2ef83d4c0cb94a1f085d30d9211ad5618c
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In Environmental Engineering
In Global and Planetary Change
In Management Science and Operations Research
Citations in the corpus
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “Furthermore, it is important to note that GNNs may not always be the optimal choice compared to other methods [317], [318], [319].”