Name
Abstract | ||
|---|---|---|
Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69835-9_74 | ADVANCES ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING (3PGCIC-2017) |
DocType | Volume | ISSN |
Conference | 13 | 2367-4512 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Rodríguez Jorge | 1 | 0 | 0.68 |
| Edgar Martínez-García | 2 | 0 | 0.68 |
| Jolanta Mizera-Pietraszko | 3 | 0 | 8.79 |
| Jirí Bíla | 4 | 0 | 1.01 |
| Rafael Torres-Córdoba | 5 | 0 | 0.68 |