# Modeling of Low Rank Time Series

@article{Cao2021ModelingOL, title={Modeling of Low Rank Time Series}, author={Wenqi Cao and Anders Lindquist and Giorgio Picci}, journal={ArXiv}, year={2021}, volume={abs/2109.11814} }

Rank-deficient stationary stochastic vector processes are present in many problems in network theory and dynamic factor analysis. In this paper we study hidden dynamical relations between the components of a discrete-time stochastic vector process and investigate their properties with respect to stability and causality. More specifically, we construct transfer functions with a full-rank input process formed from selected components of the given vector process and having a vector process of the… Expand

#### One Citation

Identification of Low Rank Vector Processes

- Computer Science, Engineering
- ArXiv
- 2021

This work considers processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable and shows that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps. Expand

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