Name
Title | ||
|---|---|---|
Feature-Based Online Representation Algorithm For Streaming Time Series Similarity Search |
Abstract | ||
|---|---|---|
With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1142/S021800142050010X | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
Pattern recognition, streaming time series, feature representation, similarity search | Journal | 34 |
Issue | ISSN | Citations |
5 | 0218-0014 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zhan | 1 | 0 | 1.01 |
| Changchang Sun | 2 | 4 | 0.72 |
| Yupeng Hu | 3 | 5 | 3.45 |
| Wei Luo | 4 | 120 | 27.50 |
| Jiecai Zheng | 5 | 0 | 0.68 |
| Xueqing Li | 6 | 6 | 5.15 |