  Autoregressive models are ubiquitous tools for the analysis of time series in
many domains such as computational neuroscience and biomedical engineering. In
these domains, data is, for example, collected from measurements of brain
activity. Crucially, this data is subject to measurement errors as well as
uncertainties in the underlying system model. As a result, standard signal
processing using autoregressive model estimators may be biased. We present a
framework for autoregressive modelling that incorporates these uncertainties
explicitly via an overparameterised loss function. To optimise this loss, we
derive an algorithm that alternates between state and parameter estimation. Our
work shows that the procedure is able to successfully denoise time series and
successfully reconstruct system parameters. This new paradigm can be used in a
multitude of applications in neuroscience such as brain-computer interface data
analysis and better understanding of brain dynamics in diseases such as
epilepsy.
