A Survey on Time-Series Distance Measures
It addresses the need for evaluating distance measures in time-series analysis across various fields, but it is incremental as a survey rather than introducing new methods.
This survey reviews over 100 state-of-the-art distance measures for time-series analysis, categorizing them into 7 types and providing mathematical frameworks and applications to guide future development.
Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the effectiveness and efficiency of these distance measures. To provide a comprehensive view of this field, this work considers over 100 state-of-the-art distance measures, classified into 7 categories: lock-step measures, sliding measures, elastic measures, kernel measures, feature-based measures, model-based measures, and embedding measures. Beyond providing comprehensive mathematical frameworks, this work also delves into the distinctions and applications across these categories for both univariate and multivariate cases. By providing comprehensive collections and insights, this study paves the way for the future development of innovative time-series distance measures.