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A comparison of AdaBoost algorithms for time series forecast combination
2016
International Journal of Forecasting
Recently, combination algorithms from machine learning classification have been extended to time series regression, most notably seven variants of the popular AdaBoost algorithm. Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms. Also, none of the algorithms have been assessed on a representative set of
doi:10.1016/j.ijforecast.2016.01.006
fatcat:jlhndcfk4fc3xpfki3lrqyha7m