Name
Abstract | ||
|---|---|---|
One common approach for exploring, approximating and estimating complex system dynamics refers to models based on elementary or atomic building block components of easier interpretation and efficient computation. We adopt a combination of wavelets, greedy approximation and subspace techniques to investigate complex systems such as speculative financial markets. These systems are endowed with multiscale and non-stationary dynamics that we aim to artificially learn by using functional approximation and optimization theory. We show that volatility dynamics are embedded in a sequence of nested informative scales and thus a multiscale estimation approach is indicated to deal with them. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1142/S0219525903000840 | ADVANCES IN COMPLEX SYSTEMS |
Keywords | Field | DocType |
atomic dictionaries,wavelets,subspace decomposition,greedy approximation | Complex system,Greedy approximation,Subspace topology,Subspace decomposition,Artificial intelligence,Functional approximation,Machine learning,Mathematics,Wavelet,Computation | Journal |
Volume | Issue | ISSN |
6 | 2 | 0219-5259 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Capobianco | 1 | 22 | 7.39 |