Balsa, CarlosDupuis, HugoBreve, Murilo MontaniniGuivarch, RonanRufino, José2025-02-122025-02-122024Balsa, Carlos; Dupuis, Hugo; Breve, Murilo Montanini; Guivarch, Ronan; Rufino, José (2024). Optimal Latent Variables Number for the Reconstruction of Time Series with PLSR. In International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023). Cham: Springer Nature, p. 193-205. ISBN 978-3-031-69227-7.978-3-031-69227-7http://hdl.handle.net/10198/31185The Partial Least Squares Regression is an efficient method for the filling of gaps in meteorological time series. It enables to reduce the dimension of the predictor dataset to a reduced number of latent variables, without loss of significant information. Defining the number of latent variables to be used is an essential aspect of the success of the method. This study is about the comparison between eight different criteria, used in the choice of latent variables. The results indicate that the criteria based on cross-validation are the most efficient, being, however, more computationally demanding.engPartial least squares regressionLatent variablesTime seriesCross validationconference paper10.1007/978-3-031-69228-4_13