14–18 Sept 2025
Piraeus, Greece
Europe/Athens timezone

Latent Structures for Serially Dependent Data

Not scheduled
20m
Piraeus, Greece

Piraeus, Greece

Statistical Process Monitoring

Speaker

Mr Moritz Bauchrowitz (Technical University of Denmark, Novo Nordisk A/S)

Description

Many chemometrics methods like Principal Component Analysis (PCA) function under the assumption of time independent observations, which may not be valid in most industrial applications. This is particularly true when PCA is employed for multivariate statistical process control. To handle time dependent data, Dynamic PCA (DPCA) has been proposed, which incorporates expanding the feature matrix with lagged versions of itself to capture time-dependent relationships. This however introduces challenges such as selecting the number of lags as a hyperparameter and the interpretation of the latent structures as they are potentially composed of numerous features including their lagged versions. In this paper, we investigate the means for proper selection of the number of lags based on the autocorrelation structure of the original features and clearer understanding of the contributions of these features and their lagged versions in the latent variables through regularization.

Classification Mainly methodology
Keywords Time Dependent Data, Dynamic PCA, Regularization

Primary author

Mr Moritz Bauchrowitz (Technical University of Denmark, Novo Nordisk A/S)

Co-authors

Murat Kulahci (Technical University of Denmark) Mr Pau Cabaneros (Novo Nordisk A/S) Mr Peter Westergaard Jakobsen (Novo Nordisk A/S) Tobias Eifler (Technical University of Denmark)

Presentation materials

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