Abstract
The analysis of time series, and in particular the identification of similar time series within a large set of time series, is an important part of visual analytics. This paper describes extensions of tree-based index structures to find self-similarities within sets of time series. It also describes filters that extend existing algorithms to better fit real-world, error-prone, incomplete data. The ability of time series clustering to detect common errors in real data is also described. These main contributions are illustrated with real data and the findings and lessons learned are summarised.
| Originalsprache | englisch |
|---|---|
| Titel | Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Herausgeber (Verlag) | SciTePress |
| Seiten | 583-592 |
| Seitenumfang | 10 |
| Band | 1, GRAPP, HUCAPP and IVAPP |
| ISBN (elektronisch) | 978-989-758-679-8 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2024 - Rome, Italien Dauer: 27 Feb. 2024 → 29 Feb. 2024 |
Konferenz
| Konferenz | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
|---|---|
| Kurztitel | VISIGRAPP 2024 |
| Land/Gebiet | Italien |
| Ort | Rome |
| Zeitraum | 27/02/24 → 29/02/24 |
ASJC Scopus subject areas
- Computergrafik und computergestütztes Design
- Maschinelles Sehen und Mustererkennung
- Human-computer interaction