Name
Title | ||
|---|---|---|
A General Approach for Supporting Time Series Matching Using Multiple-Warped Distances |
Abstract | ||
|---|---|---|
Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education. Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a plethora of dynamic time warping distances. However, the computational costs of using such elastic distances result in unacceptable response times. We thus design the first practical solution for the efficient
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eral
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ploration of time series leveraging multiple warped distances. GENEX pre-processes time series data in metric point-wise distance spaces, while providing bounds for the accuracy of corresponding analytics derived in non-metric warped distance spaces. Our empirical evaluation on 66 benchmark datasets provides a comparative study of the accuracy and response times of diverse warped distances. We show that GENEX is a versatile yet highly efficient solution for processing expensive-to-compute warped distances over large datasets, with response times 3 to 5 orders of magnitude faster than state-of-art systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2020.2998002 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Time series mining,dynamic time warping,similarity exploration | Journal | 34 |
Issue | ISSN | Citations |
4 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodica Neamtu | 1 | 9 | 4.26 |
| Ramoza Ahsan | 2 | 6 | 3.53 |
| Cuong Dinh Tri Nguyen | 3 | 0 | 0.34 |
| Charles Lovering | 4 | 3 | 2.09 |
| Elke A. Rundensteiner | 5 | 4076 | 700.65 |
| Gábor N. Sárközy | 6 | 543 | 69.69 |