Name
Abstract | ||
|---|---|---|
Time series shapelets proposes an approach to extract subsequences most suitable to discriminate time series belonging to distinct classes. Computational complexity is the major issue with shapelets: the time required to identify interesting subsequences can be intractable for large cases. In fact, it is required to evaluate all the subsequences of all the time series of the training dataset. In the literature, improvements have been proposed to accelerate the process, but few provide a solution that dramatically reduces the time required to find a solution. We propose a random-based approach that reduces the time necessary to find a solution, in our experimentation until 3 orders of magnitude compared to the original method. Based on extensive experimentations on several data sets from the literature, we show that even with a few time available, random-shapelet algorithm is able to find very competitive shapelets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DSAA.2015.7344782 | 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | Field | DocType |
shapelet discovery,time series shapelets,computational complexity,random-shapelet algorithm | Time series,Data mining,Data set,Computer science,Algorithm,Acceleration,Time complexity,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
978-1-4673-8272-4 | 5 | 0.41 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xavier Renard | 1 | 5 | 0.41 |
| Maria Rifqi | 2 | 407 | 33.64 |
| Walid Erray | 3 | 9 | 3.26 |
| Marcin Detyniecki | 4 | 330 | 39.95 |