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System identification based on Koopman operator theory has grown inpopularity recently. Spectral properties of the Koopman operator of a systemwere proven to relate to properties like invariant sets, stability,periodicity, etc. of the underlying system. Estimation of these spectralobjects has become widely accessible with the numerous algorithms developed inrecent years. We show how one such algorithm -- extended dynamic modedecomposition -- can be used on data from multiple outputs of a system. Theseoutputs that are functions of state are called observables in the literatureand could be known outputs like the state itself or unknown outputs like datafrom sensors used in systems of biological interest. To this end, weapproximate the desired observables and their iterates in time using minimizersof regularized least-squares problems which have analytic solutions withheuristic provisions for expected estimates.

Extending dynamic mode decomposition to data from multiple outputs