Miguel Pasadas

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The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent(More)
Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with(More)
In this paper we define the notion of pseudo-parallel parameterized surfaces, extending that of offset surfaces. Then we consider the problem of fitting a set of scattered points with a surface pseudo-parallel to a given reference surface. We propose a method of solution based on a modified version of the classical smoothing D m-splines over a bounded(More)