Flexible conditional density estimation for time series
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 42 references that are in the map. First posted on arXiv 23 Jan 2023.
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References · 9
- 2017Attention Is All You Needcited by 59,932
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2020CD-split and HPD-split: efficient conformal regions in high dimensionscited by 74
- 2019Flexible distribution-free conditional predictive bands using density estimatorscited by 64
- 2017Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimationcited by 34
- 2019Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inferencecited by 21
- 2016Nonparametric Conditional Density Estimation in a High-Dimensional Regression Settingcited by 20
- 2014High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computationcited by 19
- 2016A Spectral Series Approach to High-Dimensional Nonparametric Regressioncited by 8
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