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. With plenty of ways to modify the classical poses.Comparing and contrasting subtle historical patterns is central to time series analysis. These routines are appropriate for all levels of students. Rocket Yoga is a perfect balance of tempo and sequence. This makes the sequence accessible to all practitioners, even those who may have structural disabilities who would otherwise not be able to practice traditional Ashtanga Yoga methods. Students are encouraged to make their own interpretation of the traditional asana and can remove or modify binding postures. The structure is similar to that of Ashtanga Vinyasa, consisting of a warm-up (Surya Namaskar A + B), after which one continues in the standing poses, seated poses, and ending with the traditional closing series. Rocket Yoga is a dynamic and fast-paced flow of yoga. Schultz's yoga was first called "Rocket Yoga" by Bob Weir of The Grateful Dead, because Weir said, "It gets you there faster." It is composed of poses from the Ashtanga series. Rocket Yoga is based in the practice of Ashtanga Vinyasa. Rocket Yoga, also known as 'The Rocket', is a style of yoga developed by Larry Schultz in San Francisco during the 1980s.
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