Dynamic system multivariate calibration with low-sampling-rate y data

Abstract

When the data in principal component regression (PCR) or partial least squares regression (PLSR) form time series, it may be possible to improve the prediction/estimation results by utilizing the correlation between neighboring observations. The estimators may then be identified from experimental data using system identification methods. This is possible also in cases where the response variables in the experimental data are sampled at a low and possibly irregular rate, while the regressor variables are sampled at a higher rate. After a discussion of the options available, the paper shows how the autocorrelation of the regressor variables in such multirate sampling cases may be utilized by identification of parsimonious output error (OE) estimators. An example using acoustic power spectrum regressor data is finally presented.