Name
Abstract | ||
|---|---|---|
More massive volume of data are generated in many areas than ever before. However, the missing of some values in collected data always occurs in practice and challenges extracting maximal value from these large scale data sets. Nevertheless, in multivariable time series, most of the existing methods either might be infeasible or could be inefficient to predict the missing data. In this paper, we h... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBDATA.2017.2719703 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Time series analysis,Sensor phenomena and characterization,Big Data,Big data,Predictive models,Linear programming | Time series,Data mining,Data set,Spark (mathematics),Multivariable calculus,Computer science,Matrix decomposition,Synthetic data,Artificial intelligence,Missing data,Big data,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 4 | 2332-7790 |
Citations | PageRank | References |
3 | 0.37 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| WeiWei Shi | 1 | 51 | 12.26 |
| Yongxin Zhu | 2 | 13 | 5.27 |
| Philip S. Yu | 3 | 30670 | 3474.16 |
| Jiawei Zhang | 4 | 806 | 72.17 |
| Tian Huang | 5 | 53 | 7.40 |
| chang wang | 6 | 33 | 12.55 |
| Yufeng Chen | 7 | 6 | 1.30 |