Interpretable and Explainable Time Series Mining

Time series analysis has been actively explored for different machine learning tasks, such as classification and forecasting, with the best-performing methods being based on complex deep learning architectures with limited interpretability and explainability. Explainability is mainly obtained by post-hoc analysis of a complex neural network. In contrast, inter-pretability is obtained by creating the most important human-understandable features further fed into a linear model. In this tutorial, we will introduce current trends and state-of-the-art algorithms, time series classification, and forecasting methods that are interpretable-by-design and/or explainable.