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arXiv · 2111.06403

Modelling stochastic time delay for regression analysis

Juan Camilo Orduz·Aaron Pickering
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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.

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Juan Camilo Orduz, Aaron Pickering. 2021-11-11. Modelling stochastic time delay for regression analysis. https://arxiv.org/abs/2111.06403

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