Statistical characterization and optimization of artificial neural networks in time series forecasting: the one-period forecast case
Time series forecasting is an active area for the application of Artificial Neural Networks (ANNs). Although the selection of an ANN has been greatly simplified, it remains a challenge to adequately determine the ANNs parameters. In this work a method based on statistical analysis and optimization...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2006 |
| País: | México |
| Institución: | Universidad Autónoma de Nuevo León |
| Repositorio: | Redalyc-UANL |
| OAI Identifier: | oai:redalyc.org:61501106 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=61501106 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación Time Series Forecasting Artificial Neural Networks Design and Analysis of Experiments |
| Sumario: | Time series forecasting is an active area for the application of Artificial Neural Networks (ANNs). Although the selection of an ANN has been greatly simplified, it remains a challenge to adequately determine the ANNs parameters. In this work a method based on statistical analysis and optimization techniques is proposed to select the ANN´s parameters for application in time series forecasting. The results on the successful application of the method in a real demand forecasting problem for the telecommunications industry are also reported. . |
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