Name
Abstract | ||
|---|---|---|
ABSTRACTTime series data are ubiquitous. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. This has resulted in a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. This demands development of new tools and solutions. The goals of this workshop are to: (1) highlight the significant challenges that underpin learning and mining from time series data (e.g. irregular sampling, spatiotemporal structure, and uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) synergize the research activities and discuss both new and open problems in time series analysis and mining. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447548.3469485 | Knowledge Discovery and Data Mining |
Keywords | DocType | Citations |
time-series analysis, temporal data mining, COVID-19 time series | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanjay Purushotham | 1 | 244 | 11.70 |
| Yaguang Li | 2 | 177 | 10.43 |
| Zhengping Che | 3 | 55 | 4.25 |