Name
Title | ||
|---|---|---|
Grouping multivariate time series variables: applications to chemical process and visual field data |
Abstract | ||
|---|---|---|
In many industrial and medical applications it is important to identify relationships in multivariate time series (MTS) variables in as short a time as possible. Within this paper, we present a method for decomposing high dimensional MTS into mutually exclusive subsets of variables where within-group dependencies are high and between group dependencies are low. The method involves the use of two evolutionary computation techniques, which find an approximate solution to an otherwise NP-hard problem. We apply the proposed method to two real-world datasets, a chemical process MTS from an oil refinery and an ophthalmic MTS regarding glaucomatous deterioration. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0950-7051(01)00091-0 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Grouping,Multivariate time series,Evolutionary computation | Journal | 14 |
Issue | ISSN | Citations |
3 | 0950-7051 | 3 |
PageRank | References | Authors |
0.70 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Swift | 1 | 427 | 31.32 |
| Allan Tucker | 2 | 108 | 14.47 |
| Nigel Martin | 3 | 31 | 3.68 |
| Xiaohui Liu | 4 | 5042 | 269.99 |