Name
Abstract | ||
|---|---|---|
Dynamic time warping (DTW) has been widely used in various domains of daily life. Essentially, DTW is a non-linear point-to-point matching method under time consistency constraints to find the optimal path between two temporal sequences. Although DTW achieves a globally optimal solution, it does not naturally capture locally reasonable alignments. Concretely, two points with entirely dissimilar local shape may be aligned. To solve this problem, we propose a novel weighted DTW based on local slope feature (LSDTW), which enhances DTW by taking regional information into consideration. LSDTW is inherently a DTW algorithm. However, it additionally attempts to pair locally similar shapes, and to avoid matching points with distinct neighborhood slopes. Furthermore, when LSDTW is used as a similarity measure in the popular nearest neighbor classifier, it beats other distance-based methods on the vast majority of public datasets, with significantly improved classification accuracies. In addition, case studies establish the interpretability of the proposed method.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3357917 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
classification, dynamic time warping, local slope feature, time series alignment | Data mining,Dynamic time warping,Computer science,Time series classification | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jidong Yuan | 1 | 18 | 6.45 |
| Qianhong Lin | 2 | 1 | 0.36 |
| Wei Zhang | 3 | 1 | 0.70 |
| Zhihai Wang | 4 | 425 | 28.26 |