论文标题
时间序列使用随机森林的分位数回归
Time series quantile regression using random forests
论文作者
论文摘要
我们讨论了Athey等人（2019年）提出的广义随机森林（GRF）的应用，以分别回归时间序列数据。我们提取了I.I.D的GRF一致性的理论结果。数据到时间序列数据。特别是，在主要定理中，仅基于戴维斯和尼尔森（2020）的时间序列数据的一般假设，以及Athey等人（2019年）中的树木，我们表明TSQRF（时间序列回归森林）估计量是一致的。戴维斯（Davis）和尼尔森（Nielsen，2020）还使用随机森林（RF）来讨论了时间序列数据的估计问题，但是GRF处理的RF的施工程序本质上是不同的，并且在整个理论证明中都使用了不同的想法。此外，进行了模拟和实际数据分析。在模拟中，在时间序列模型下评估了条件分位数估计的准确性。在使用Nikkei股票平均值的实际数据中，我们的估计器在波动性方面比其他估计值更敏感，从而防止了风险低估。
We discuss an application of Generalized Random Forests (GRF) proposed by Athey et al.(2019) to quantile regression for time series data. We extracted the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data in Davis and Nielsen (2020), and trees in Athey et al.(2019), we show that the tsQRF (time series Quantile Regression Forests) estimator is consistent. Davis and Nielsen (2020) also discussed the estimation problem using Random Forests (RF) for time series data, but the construction procedure of the RF treated by the GRF is essentially different, and different ideas are used throughout the theoretical proof. In addition, a simulation and real data analysis were conducted.In the simulation, the accuracy of the conditional quantile estimation was evaluated under time series models. In the real data using the Nikkei Stock Average, our estimator is demonstrated to be more sensitive than the others in terms of volatility, thus preventing underestimation of risk.
