A literature survey of shapelet quality measures for time series classification

With the rapid development of the Internet of Things, time series classification (TSC) has gained significant attention from researchers due to its applications in various real-world fields, including electroencephalogram/electrocardiogram classification, emotion recognition, and error message detection. To improve classification performance, numerous TSC methods have been proposed in recent years. Among these, shapelet-based TSC methods are particularly notable for their intuitive interpretability. A critical task within these methods is evaluating the quality of candidate shapelets. This paper provides a comprehensive survey of the state-of-the-art measures for assessing shapelet quality. To present a structured overview, we begin by proposing a taxonomy of these measures, followed by a detailed description of each one. We then discuss these measures, highlighting the challenges faced by current research and offering suggestions for future directions. Finally, we summarize the findings of this survey. We hope that this work will serve as a valuable resource for researchers in the field.