Modeling of time series using random forests: theoretical developments

Random forests, originally introduced by Breiman [8], constitute an ensemble learning algorithm for classification and regression, which produces predictions by first growing a large number of randomized decision trees [9] and, then, aggregates the results. Since its introduction, the algorithm has been applied in various fields such as object recognition [25], bioinformatics [12], ecology [10, 22] and finance [15, 18], and the evidence is strong: with very little tuning, random forests are able to deliver a flexible tool for prediction which is fully comparable with other state-of-the-art algorithms. In fact, Howard and Bowles [17] claim that random forests have been the most successful general-purpose algorithm in recent times. While many successful applications indicate the wide applicability of random forests, only little theoretical work exists to support this impression.