Interpretable Feature Construction for Time Series Extrinsic Regression

• URL: http://arxiv.org/abs/2103.10247v1
• Date: Mon, 15 Mar 2021 08:12:19 GMT
• Title: Interpretable Feature Construction for Time Series Extrinsic Regression
• Authors: Dominique Gay, Alexis Bondu, Vincent Lemaire, Marc Boull\'e
• Abstract summary: In some application domains, it occurs that the target variable is numerical and the problem is known as time series extrinsic regression (TSER) We suggest an extension of a Bayesian method for robust and interpretable feature construction and selection in the context of TSER. Our approach exploits a relational way to tackle with TSER: (i), we build various and simple representations of the time series which are stored in a relational data scheme, then, (ii), a propositionalisation technique is applied to build interpretable features from secondary tables to "flatten" the data.
• Score: 0.028675177318965035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised learning of time series data has been extensively studied for the case of a categorical target variable. In some application domains, e.g., energy, environment and health monitoring, it occurs that the target variable is numerical and the problem is known as time series extrinsic regression (TSER). In the literature, some well-known time series classifiers have been extended for TSER problems. As first benchmarking studies have focused on predictive performance, very little attention has been given to interpretability. To fill this gap, in this paper, we suggest an extension of a Bayesian method for robust and interpretable feature construction and selection in the context of TSER. Our approach exploits a relational way to tackle with TSER: (i), we build various and simple representations of the time series which are stored in a relational data scheme, then, (ii), a propositionalisation technique (based on classical aggregation / selection functions from the relational data field) is applied to build interpretable features from secondary tables to "flatten" the data; and (iii), the constructed features are filtered out through a Bayesian Maximum A Posteriori approach. The resulting transformed data can be processed with various existing regressors. Experimental validation on various benchmark data sets demonstrates the benefits of the suggested approach.

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