Name
Abstract | ||
|---|---|---|
In this article we propose a concept of ensemble methods based on deconvolution with state space and MLP neural network approach. Having a few prediction models we treat their results as a multivariate variable with latent components having destructive or constructive impact on prediction. The latent component classification is performed using novel variability measure derived from Hurst exponent. The validity of our concept is presented on the real problem of load forecasting in the Polish power system. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41013-0_16 | Communications in Computer and Information Science |
Keywords | Field | DocType |
state space approach,Hurst exponent,Independent Component Analysis,ensemble methods | Statistical physics,Computer science,Multivariate statistics,Hurst exponent,Deconvolution,Detrended fluctuation analysis,Independent component analysis,Artificial neural network,State space,Ensemble learning | Conference |
Volume | ISSN | Citations |
383 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryszard Szupiluk | 1 | 38 | 8.97 |
| Tomasz Zabkowski | 2 | 32 | 11.28 |