Name
Abstract | ||
|---|---|---|
Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2623330.2623613 | KDD |
Keywords | Field | DocType |
data mining,shapelets,supervised feature extraction,time-series classification | Data mining,Pattern recognition,Computer science,Artificial intelligence,Discriminative model,Machine learning,Time series classification | Conference |
Citations | PageRank | References |
38 | 1.18 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josif Grabocka | 1 | 106 | 14.69 |
| Nicolas Schilling | 2 | 99 | 9.24 |
| Martin Wistuba | 3 | 154 | 19.66 |
| Lars Schmidt-Thieme | 4 | 3802 | 216.58 |