Flexible conditional density estimation for time series
Methodology
2023-01-25 v1 Machine Learning
Machine Learning
Abstract
This paper introduces FlexCodeTS, a new conditional density estimator for time series. FlexCodeTS is a flexible nonparametric conditional density estimator, which can be based on an arbitrary regression method. It is shown that FlexCodeTS inherits the rate of convergence of the chosen regression method. Hence, FlexCodeTS can adapt its convergence by employing the regression method that best fits the structure of data. From an empirical perspective, FlexCodeTS is compared to NNKCDE and GARCH in both simulated and real data. FlexCodeTS is shown to generally obtain the best performance among the selected methods according to either the CDE loss or the pinball loss.
Cite
@article{arxiv.2301.09671,
title = {Flexible conditional density estimation for time series},
author = {Gustavo Grivol and Rafael Izbicki and Alex A. Okuno and Rafael B. Stern},
journal= {arXiv preprint arXiv:2301.09671},
year = {2023}
}
Comments
19 pages, 7 figures