A Survey on Time-Series Distance Measures
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916545602519040 |
|---|---|
| author | Paparrizos, John Li, Haojun Yang, Fan Wu, Kaize d'Hondt, Jens E. Papapetrou, Odysseas |
| author_facet | Paparrizos, John Li, Haojun Yang, Fan Wu, Kaize d'Hondt, Jens E. Papapetrou, Odysseas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Time-Series Distance Measures Paparrizos, John Li, Haojun Yang, Fan Wu, Kaize d'Hondt, Jens E. Papapetrou, Odysseas Databases Artificial Intelligence Machine Learning 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. |
| title | A Survey on Time-Series Distance Measures |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.20574 |