A New Methodology for Neural Network Training Ensures Error Reduction in Time Series Forecasting

Artificial Neural Networks (ANN) consists of some components, such as architecture and learning algorithm. These components have a significant effect on the performance of the ANN, but finding good parameters is a difficult task to achieve. An important requirement for this task is to ensure the reduction of error when inputs and/or hidden neurons are added. In practice, it is assumed that this requirement is always true, but usually it is false. In this paper, we propose a new algorithm that ensures error decrease when input variables and/or hidden neurons are added to the neural network. The behavior of two traditional algorithms and the proposed algorithm in the forecast of Airline time series were compared. The empirical results indicate that the proposed algorithm allows a steady decrease of fit error in all cases, where de most important and differentiable feature is the fact that reach values close to zero, which is not true for the other algorithms. Therefore, it can be used as a suitable alternative algorithm, especially when it needs a good fit.

• Autores: Sánchez-Sánchez, Paola A.; García-González, José Rafael
• Fecha de publicación: 2017
• Institución: Universidad Simón Bolívar
• Repositorio: Repositorio Digital USB
• Idioma: eng
• OAI Identifier: oai:bonga.unisimon.edu.co:20.500.12442/1735
• Acceso en línea: http://hdl.handle.net/20.500.12442/1735
• Palabra clave: Artificial Neural Networks (ANN); Time Series Forecasting; Learning Algorithms; Error Reduction
• License: http://purl.org/coar/access_right/c_abf2