Time series quantile regression using random forests. (arXiv:2211.02273v1 [math.ST])

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 …

arxiv math quantile random random forests regression series time series