On Data-driven Multistep Subspace-based Linear Predictors

In English The focus of this contribution is the estimation of multi-step-ahead
linear multivariate predictors of the output making use of finite
input-output data sequences. Different strategies will be presented, the common factor being the exploitations of geometric operations on appropriate subspaces spanned by the data. In order to test the capabilities of the proposed methods in predicting new data, a real-life example, namely, the case of blood glucose prediction in Type 1 Diabetes patients, is provided.