Forecast Combinations: An over 50-Year Review - Wang, Xiaoqian; Hyndman, Rob J.; Feng Li; Kang, Yanfei (International Journal of Forecasting, 2023)
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of mainstream forecasting research and activities. Combining multiple forecasts produced for a target time series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby avoiding the need to identify a single “best” forecast. Combination schemes have evolved from simple combination methods without estimation to sophisticated techniques 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 and a 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 crucial issues concerning the utility of forecast combinations are also surveyed. Finally, we conclude with current research gaps and potential insights for future research.
Keywords: Combination forecast; Cross learning; Forecast combination puzzle; Forecast ensembles; Model averaging; Open-source software; Pooling; Probabilistic forecasts; Quantile forecasts