Abstract
In this paper, we propose a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective to estimate a fully-interacting reduced dimensional dynamic model. This algorithm is an advancement of the dynamic inner canonical correlation analysis (DiCCA) algorithm, which builds univariate latent autoregressive models that are non-interacting. The dynamic latent variable scores of the proposed algorithm are enforced to be orthogonal or contemporaneously independent, similar to those of DiCCA. An application case study on an industrial dataset is given to illustrate the superiority of the proposed algorithm. The reduced-dimensional latent dynamic model has potential applications for prediction, control, and diagnosis of systems with rich sensors, such as industrial internet of things.
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