Linear Model Regression on Time-series Data: Non-asymptotic Error Bounds and Applications

By Atiye Alaeddini, Siavash Alemzadeh, Afshin Mesbahi, Mehran Mesbahi. 2018

18 citations recorded by the source corpus.

Data-driven methods for modeling dynamic systems have recently received considerable attention as they provide a mechanism for control synthesis directly from the observed time-series data. In the absence of prior assumptions on how the time-series had been generated, regression on the system model has been particularly popular. In the linear case, the resulting least squares setup for model regression, not only provides a computationally viable method to fit a model to the data, but also provides useful insights into the modal properties of the underlying dynamics. Although probabilistic estimates for this model regression have been reported, deterministic error bounds have not been examined in the literature, particularly as they pertain to the properties of the underlying system. In this paper, we provide deterministic non-asymptotic error bounds for fitting a linear model to observed time-series data, with a particular attention to the role of symmetry and eigenvalue multiplicity in the underlying system matrix.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics, Statistical Learning Theory, Time-Series & Forecasting

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Citations in the corpus

A Convergence Theory for Deep Learning via Over-Parameterization (2018, background): “Recent works in this line of research include (Alaeddini et al., 2018; Arora et al., 2018b; Dean et al., 2017; 2018; Hardt et al., 2018; Hazan et al., 2017; 2018; Marecek & Tchrakian, 2018; Oymak & Ozay, 2018; Simchowitz et al., 2018).”