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