Gregorio Ismael Sainz Palmero

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The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of(More)
Multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) have been widely applied to the statistical process monitoring and their effectiveness for fault detection is well recognized, but they have a drawback: the fault diagnosis. In this paper a new method to detect and diagnosis faults is proposed that is(More)
The main objective of ‘‘time series analysis’’ is to discover the underlying structure of the time series, and thus, become able to forecast its ‘‘future values’’. This process makes it possible to predict, control or simulate variables. Most of the time series modelling procedures try to forecast future values from lagged ones. Thus, the selection of the(More)