Flexible conditional density estimation for time series

Autor:	Grivol, Gustavo, Izbicki, Rafael, Okuno, Alex A., Stern, Rafael B.
Rok vydání:	2023
Předmět:	Statistics - Methodology Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 19 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.09671 Zobrazit plný text záznamu View this record from Arxiv