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One of the most important uses of artificial neural networks is to forecast non-linear time series, although model-building issues, such as input selection, model complexity and parameters estimation, remain without a satisfactory solution. More of research efforts are devoted to solve these issues. However, other models emerged from statistics would be more appropriated than neural networks for forecasting, in the sense that the process of model specification is based entirely on statistical criteria. Multivariate adaptive regression splines MARS is a statistical model commonly used for solving nonlinear regression problems, and it is possible to use it for forecasting time series. Nonetheless, there is a lack of studies comparing the results obtained using MARS and neural network models, with the aim of determinate which model is better. In this paper, we forecast four nonlinear time series using MARS and we compare the obtained results against the reported results in the technical literature when artificial neural networks and the ARIMA approach are used. The main finding in this research, it is that for all considered cases, the forecasts obtained with MARS are lower in accuracy in relation to the other approaches.

Tipo de documento: Artículo - Article

Información adicional: Derechos de autor reservados

Palabras clave: Artificial neural networks, comparative studies, ARIMA models, nonparametric methods.





Source: http://www.bdigital.unal.edu.co


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DYNA http:--dyna.medellin.unal.edu.co- Nonlinear time series forecasting using MARS Predicción de series de tiempo no lineales usando MARS Juan David Velásquez-Henao a, Carlos Jaime Franco-Cardona b and Paula Andrea Camacho c a Facultad de Minas, Universidad Nacional de Colombia, Colombia.
jdvelasq@unal.edu.co Facultad de Minas, Universidad Nacional de Colombia, Colombia.
cjfranco@unal.edu.co c Facultad de Minas, Universidad Nacional de Colombia, Colombia.
pcamach@unal.edu.co b Received: September 17th, 2012.
Received in revised form: December 1th, 2013.
Accepted: March 3th, 2014. Abstract One of the most important uses of artificial neural networks is to forecast non-linear time series, although model-building issues, such as input selection, model complexity and parameters estimation, remain without a satisfactory solution.
More of research efforts are devoted to solve these issues.
However, other models emerged from statistics would be more appropriated than neural networks for forecasting, in the sense that the process of model specification is based entirely on statistical criteria.
Multivariate adaptive regression splines (MARS) is a statistical model commonly used for solving nonlinear regression problems, and it is possible to use it for forecasting time series. Nonetheless, there is a lack of studies comparing the results obtained using MARS and neural network models, with the aim of determinate which model is better.
In this paper, we forecast four nonlinear time series using MARS and we compare the obtained results against the reported results in the technical literature when artificial neural networks and the ARIMA approach are used.
The main finding in this research, it is that for all considered cases, the forecasts obtained with MARS are lower in accuracy in relation to the other approaches. Keywords: Artificial neural networks; comparative studies; ARIMA models; nonparametric methods. Resumen Uno de los usos más importantes de las redes neuronal...






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