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.
Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions
University of Texas at Austin（德克萨斯大学奥斯汀分校）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）
EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography
Centre for Digital Health Interventions, ETH Zurich（数字健康干预中心，苏黎世联邦理工学院）；Centre for Digital Health Interventions, University of St.Gallen（数字健康干预中心，圣加伦大学）
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
Department of Computer Science and Numerical Analysis, University of Cordoba（科塔达大学计算机科学与数值分析系）；School of Computing Sciences, University of East Anglia（东安格利亚大学计算科学学院）
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Department of Computer Science, Duke University, Durham, USA（杜克大学计算机科学系，美国达勒姆）；Department of Computer Science, University of British Columbia, Vancouver, Canada（不列颠哥伦比亚大学计算机科学系，加拿大温哥华）
Embedding interpretable $\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging
Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Germany（医学生物度量与统计研究所（IMBI）、医学学院与医疗中心、弗赖堡大学）；Institute of Physiology I, Faculty of Medicine, University of Freiburg, Germany（医学生理学研究所I、医学学院、弗赖堡大学）；Department of Computer Science, University of Freiburg, Germany（计算机科学系、弗赖堡大学）；Freiburg Center for Data Analysis, Modeling and AI (FDMAI), University of Freiburg, Germany（弗赖堡数据分析、建模与人工智能中心（FDMAI）、弗赖堡大学）；Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Germany（整合生物信号研究中心（CIBSS）、弗赖堡大学）
Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values
Towards symbolic regression for interpretable clinical decision scores
Federal University of ABC（ABC联邦大学）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Biomedical Informatics, University of Pennsylvania（宾夕法尼亚大学生物医学信息学研究所）；Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression
The University of Washington eScience Institute（华盛顿大学电子科学研究所）；University of Washington（华盛顿大学）；Center for Magnetic Resonance Research（磁共振研究中心）；University of Minnesota（明尼苏达大学）
Are Linear Regression Models White Box and Interpretable?
University of Leicester（莱斯特大学）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴特保健国民保健信托基金）；University of Zakho（扎胡大学）；St Bartholomew’s Hospital（圣巴塞洛缪医院）；William Harvey Research Institute（威廉·哈维研究所）；NIHR Barts Biomedical Research Centre（英国国家卫生研究院巴特生物医学研究中心）；Barts Heart Centre（巴特心脏中心）
Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
Interpretable Robotic Friction Learning via Symbolic Regression
Ludwig-Maximilians-Universität München（慕尼黑路易斯-马克西米利安大学）；German Aerospace Center（德国航空航天中心）；University of Tromsø（特罗姆瑟大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
Federal University of ABC（ABC联邦大学）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University of Applied Sciences Upper Austria（上奥地利应用科学大学）；Flatiron Institute（熨斗研究所）；Princeton University（普林斯顿大学）；Universidade de Lisboa（里斯本大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of Utah（犹他大学）；Meta；Victoria University of Wellington（惠灵顿维多利亚大学）；University of otago（奥塔哥大学）；Universität Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Abzu AI（Abzu人工智能公司）
