Time series forecasting based on parallel neural network
| dc.contributor.author | J. M. Górriz, C. G. Puntonet, M. Salmerón, J. Ortega, M. Aldasht | |
| dc.date.accessioned | 2018-03-13T08:32:32Z | |
| dc.date.accessioned | 2022-05-22T08:28:49Z | |
| dc.date.available | 2018-03-13T08:32:32Z | |
| dc.date.available | 2022-05-22T08:28:49Z | |
| dc.date.issued | 2004-02-29 | |
| dc.description.abstract | In this paper we show a Parallel Neural Network (Cross-over Prediction Model) for time series forecasting implemented in PVM (”Parallel Virtual Machine”) and MPI (”Message Passing Interface”), in order to reduce computational time. Parallelization is achieved twofold: (a) updating autoregressive parameters using a genetic algorithm (GA) and (b) evaluating the overall prediction function via a parallel neural network. We implement the GA in two popular architectures of parallel processors (i.e hypercube and 2D-mesh) and discuss their time efficiency. | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/7977 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE EIS-2004, 29-febrero al 2 de Marzo, Madeira-Portugal. | en_US |
| dc.subject | Artificial Neural Networks (ANNS), Auto-Regressive Models (AR), Parallel Virtual Machine (PVM), Array and Hypercube Networks, Mask Functions, Quicksort, Genetic Algorithms | en_US |
| dc.title | Time series forecasting based on parallel neural network | en_US |
| dc.type | Article | en_US |