Name
Abstract | ||
|---|---|---|
We present a method combining ideas from the theory of operator-valued kernels with delay-coordinate embedding techniques in dynamical systems capable of identifying spatiotemporal patterns, without prior knowledge of the state space or the dynamical laws of the system generating the data. The approach is particularly powerful for systems in which characteristic patterns cannot be readily decomposed into temporal and spatial coordinates. Using simulated and observed sea-surface temperature data, we show our approach reveals coherent patterns of intermittent character with significantly higher skill than conventional analytical methods based on decomposing signals into separable spatial and temporal patterns. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SSP.2018.8450704 | 2018 IEEE Statistical Signal Processing Workshop (SSP) |
Keywords | Field | DocType |
Signal processing,kernel methods,vector-valued functions,multivariate time series,dynamical systems,spatiotemporal patterns | Signal processing,Embedding,Spatial reference system,Computer science,Algorithm,Separable space,Dynamical systems theory,Vector-valued function,Kernel method,State space | Conference |
ISBN | Citations | PageRank |
978-1-5386-1572-0 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| joanna slawinska | 1 | 4 | 1.81 |
| Abbas Ourmazd | 2 | 0 | 0.34 |
| Dimitrios Giannakis | 3 | 5 | 2.60 |