Name
Abstract | ||
|---|---|---|
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the "variable groupings" problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping method. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/3477.915346 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
evolutionary computation,time series,correlation,evolutionary programming,multivariate time series,variable groupings | Computer science,Multivariate statistics,Evolutionary computation,Correlation,Artificial intelligence,Evolutionary programming,Strengths and weaknesses,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 2 | 1083-4419 |
Citations | PageRank | References |
27 | 1.76 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A Tucker | 1 | 29 | 3.81 |
| Stephen Swift | 2 | 427 | 31.32 |
| Xiaohui Liu | 3 | 200 | 12.75 |