Name
Abstract | ||
|---|---|---|
The paper presents a method for time series prediction using local dynamic modeling. After embedding the input data in a reconstruction space using a memory structure, a self-organizing map (SOM) derives a set of local models from these data. Afterwards, a set of single layer neural networks, trained optimally with a system of linear equations, is applied at the SOM's output. The goal of the last network is to fit a local model from the winning neuron and a set of neighbours of the SOM map. Finally, the performance of the proposed method was validated using two chaotic time series. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-46084-5_153 | ICANN |
Keywords | Field | DocType |
input data,linear equation,local model,last network,time series prediction,self-organizing maps,local modeling,self-organizing map,chaotic time series,local dynamic modeling,som map,single layer neural networks,linear equations,neural network | Time series,Data modeling,Embedding,System of linear equations,Pattern recognition,Computer science,Self-organizing map,Local area network,Artificial intelligence,Chaotic,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
2415 | 0302-9743 | 3-540-44074-7 |
Citations | PageRank | References |
5 | 0.85 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oscar Fontenla-Romero | 1 | 337 | 39.49 |
| Amparo Alonso-Betanzos | 2 | 885 | 76.98 |
| Enrique Castillo | 3 | 555 | 59.86 |
| Jose C. Principe | 4 | 2295 | 282.29 |
| Bertha Guijarro-Berdiñas | 5 | 296 | 34.36 |