Name
Abstract | ||
|---|---|---|
Time series forecasting with the use of Artificial Neural Networks (ANN), in special with self-organized maps (SOM), has been explored in the literature with good results. One good strategy for improving computational cost and specialization of SOMs in general is constructing it via hierarchical structures. This work presents four different heuristics for constructing hierarchical SOMs for time series prediction, evaluating their computational cost and forecast precision and providing insight on future enhancements. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICTAI.2011.180 | ICTAI |
Keywords | Field | DocType |
self-organized hierarchical methods,good strategy,forecast precision,different heuristics,artificial neural networks,hierarchical structure,time series forecasting,computational cost,hierarchical soms,time series prediction,good result,self organization,vectors,time series analysis,artificial neural network,time series,vegetation,prototypes,forecasting,heuristic algorithm | Time series,Computer science,Heuristics,Artificial intelligence,Artificial neural network,Machine learning,Self organized map | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuad M. Abinader Jr. | 1 | 162 | 15.49 |
| Alynne C. S. de Queiroz | 2 | 0 | 0.34 |
| Daniel W. Honda | 3 | 0 | 0.34 |