<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, Xiaoqian</style></author><author><style face="normal" font="default" size="100%">Hyndman, Rob J.</style></author><author><style face="normal" font="default" size="100%">Feng Li</style></author><author><style face="normal" font="default" size="100%">Kang, Yanfei</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forecast Combinations: An over 50-Year Review</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Forecasting</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Combination forecast</style></keyword><keyword><style  face="normal" font="default" size="100%">Cross learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecast combination puzzle</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecast ensembles</style></keyword><keyword><style  face="normal" font="default" size="100%">Model averaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Open-source software</style></keyword><keyword><style  face="normal" font="default" size="100%">Pooling</style></keyword><keyword><style  face="normal" font="default" size="100%">Probabilistic forecasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantile forecasts</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2205.04216</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">1518–1547</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>