Regression analysis is the statistical process used to estimate the relationship between a dependent variable and one or more independent variables. In machine learning, typical statistical approaches to regression such as linear regression are often replaced with symbolic learning, such as decision tree regression, to capture non-linear behaviour while keeping the interpretability of the results. For temporal series, regression is sometimes enhanced by using historical values of the independent variables. In this paper, we show how temporal regression can be handled by a symbolic learner based on interval temporal logic decision trees.
Lucena-Sanchez, E., Sciavicco, G., Stan, I. (2020). Symbolic learning with interval temporal logic: The case of regression. In Proceedings of the 2nd Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis hosted by the Bolzano Summer of Knowledge 2020 (BOSK 2020) (pp.5-9). CEUR-WS.
Symbolic learning with interval temporal logic: The case of regression
Stan I. E.
2020
Abstract
Regression analysis is the statistical process used to estimate the relationship between a dependent variable and one or more independent variables. In machine learning, typical statistical approaches to regression such as linear regression are often replaced with symbolic learning, such as decision tree regression, to capture non-linear behaviour while keeping the interpretability of the results. For temporal series, regression is sometimes enhanced by using historical values of the independent variables. In this paper, we show how temporal regression can be handled by a symbolic learner based on interval temporal logic decision trees.
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