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Publication
Optimal Latent Variables Number for the Reconstruction of Time Series with PLSR
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Advisor(s)
Abstract(s)
The 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.
Description
Keywords
Partial least squares regression Latent variables Time series Cross validation
Citation
Balsa, 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.
Publisher
Springer Nature