论文标题
使用随机森林的时间序列建模：理论发展
Modeling of time series using random forests: theoretical developments
论文作者
论文摘要
在本文中，我们研究了非线性时间序列建模框架内随机森林的渐近特性。尽管随机森林已成功地应用于各个领域，但在时间序列设置中的使用尚未考虑理论上的理由。在轻度条件下，我们证明了基于非线性自回归过程的回归树的浓度均匀浓度不等式，随后，我们使用该结果证明了大量随机森林的一致性。结果由各种模拟支持。
In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling. While random forests have been successfully applied in various fields, the theoretical justification has not been considered for their use in a time series setting. Under mild conditions, we prove a uniform concentration inequality for regression trees built on nonlinear autoregressive processes and, subsequently, we use this result to prove consistency for a large class of random forests. The results are supported by various simulations.
