In this paper, we propose a novel shapelet-based classifier, called Perceptual Position-aware Shapelet Network (PPSN), to effectively discover and optimize the shapelets. However, these shapelet-based methods still have some problems in both shapelet initialization and learning shapelet phases that limit their performances. Recently, time series classifiers based on shapelets have gained interest from the community thanks to their high accuracy and interpretable results. Shapelets are time series subsequences that effectively distinguish time series classes. Especially in the challenging leave-sensors-out setting where a subset of variables is masked during testing, the performance improvement is up to 54.0\% in absolute F1 score points. Despite its simplicity, we show that it substantially outperforms state-of-the-art specialized algorithms on several popular healthcare and human activity datasets. Our approach largely simplifies algorithm designs without assuming prior knowledge and can be potentially extended as a general-purpose framework. This paper studies the problem from a whole new perspective: transforming irregularly sampled time series into line graph images and adapting powerful vision transformers to perform time series classification in the same way as image classification. Although different highly-customized methods have been proposed to tackle irregularity, how to effectively model their complicated dynamics and high sparsity is still an open problem. Irregularly sampled time series are becoming increasingly prevalent in various domains, especially in medical applications. ![]() Moreover, we show that in addition to being visually and conceptually interpretable, our approach performs better than the state-of-the-art algorithms in terms of proximity, sparsity, and second in terms of plausibility. We test TeRCE on five benchmark datasets from the UEA archive and prove that it produces high-quality counterfactuals. Thus, they can highly increase the interpretability of black-box models. Counterfactual explanations indicate how should the input change such that the decision output changes too. In this work, we aim to exploit the discriminative power of shapelets and temporal rules in time series mining and capitalize on their inherent interpretability to develop a model-agnostic, temporal rule counterfactual explainer (TeRCE) for multivariate time series datasets. However, the literature is rather scarce when it comes to time series data, and even more so in the context of multivariate time series. The two main categories of solutions are 1) developing fully transparent algorithms and 2) providing post hoc explanations. In order to reduce models’ opacity and overpass this challenge, major efforts that aim to increase stakeholders’ trust and ensure the fairness of decisions are being made by the data mining community under the Explainable Artificial Intelligence (XAI) paradigm. The black-box nature of machine learning models is the main reason impeding their full adoption in decision-making processes. Released in the publicly domain, we are hopeful that SLD will enhance the standard toolbox used in classification, clustering and inference problems in time series analysis. We demonstrate that the new tool is at par or better in classification accuracy, while being significantly faster in comparable implementations. in Pattern Recognit 44(3):678–693, 2011) with synthetic data, real-world applications with electroencephalogram data and in gait recognition, and on diverse time-series classification problems from the University of California, Riverside time series classification archive (Thanawin Rakthanmanon and Westover). We compare the performance of SLD against the state of the art approaches, e.g., dynamic time warping (Petitjean et al. Using this notion of process divergence, we craft a measure of deviation on finite sample paths which we call the sequence likelihood divergence (SLD) which approximates a metric on the space of the underlying generators within a well-defined class of discrete-valued stochastic processes. Our core idea here is the generalization of the Kullback–Leibler divergence, often used to compare probability distributions, to a notion of divergence between finite-valued ergodic stationary stochastic processes. The proposed measure is universal in the sense that we can compare data streams without any feature engineering step, and without the need of any hyper-parameters. ![]() ![]() Here we introduce a new approach to quantify deviations in the underlying hidden stochastic generators of sequential discrete-valued data streams. ![]() Comparing and contrasting subtle historical patterns is central to time series analysis.
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