Modelling stochastic time delay for regression analysis
Methodology
2021-11-15 v1
Abstract
Systems with stochastic time delay between the input and output present a number of unique challenges. Time domain noise leads to irregular alignments, obfuscates relationships and attenuates inferred coefficients. To handle these challenges, we introduce a maximum likelihood regression model that regards stochastic time delay as an "error" in the time domain. For a certain subset of problems, by modelling both prediction and time errors it is possible to outperform traditional models. Through a simulated experiment of a univariate problem, we demonstrate results that significantly improve upon Ordinary Least Squares (OLS) regression.
Cite
@article{arxiv.2111.06403,
title = {Modelling stochastic time delay for regression analysis},
author = {Juan Camilo Orduz and Aaron Pickering},
journal= {arXiv preprint arXiv:2111.06403},
year = {2021}
}
Comments
GitHub repository https://github.com/aaron1rcl/tvs_regression