Name
Abstract | ||
|---|---|---|
The goal of our approach is the determination of the order of the Markov process which explains the statistical dependencies
of an observed time series. Our method measures the information flow of the time series indirectly via higher order cumulants
considering linear and nonlinear correlations. The main point of our method, which is an extension of the method of surrogate
data, is that the time series is tested against a hierarchy of nonlinear Markov processes, whose probability densities are
estimated by neural networks.
|
Year | DOI | Venue |
|---|---|---|
1997 | 10.1007/BFb0052117 | Foundations of Computer Science: Potential - Theory - Cognition |
Keywords | Field | DocType |
nonlinear markovian characterization,neural networks,time series,information flow,probability density,surrogate data,higher order,cumulant,neural network,markov process | Information flow (information theory),Applied mathematics,Discrete mathematics,Nonlinear system,Markov process,Computer science,Cumulant,Artificial intelligence,Artificial neural network,Surrogate data,Hierarchy,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-63746-X | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Schittenkopf | 1 | 55 | 6.95 |
| Gustavo Deco | 2 | 1004 | 156.20 |