Abstract

Autoregressive modelling provides a powerful and flexible parametric approach to modelling uni- or multi-variate time-series data. AR models have mathematical links to linear time- invariant systems, digital filters and Fourier based frequency analyses. As such, a wide range of time-domain and frequency-domain metrics can be readily derived from the fitted au- toregressive parameters. These approaches are fundamental in a wide range of science and engineering fields and still undergoing active development. SAILS (Spectral Analysis in Linear Systems) is a python package which implements such methods and provides a basis for both the straightforward fitting of AR models as well as exploration and development of newer methods, such as the decomposition of autoregressive parameters into eigenmodes.

Highlights

  • Autoregressive modelling provides a powerful and flexible parametric approach to modelling unior multi-variate time-series data

  • A wide range of timedomain and frequency-domain metrics can be readily derived from the fitted autoregressive parameters

  • Advanced exploration of the spectral content of the model is provided via a modal decomposition of the fitted autoregressive parameters (Neumaier & Schneider, 2001), an alternative to analyses which require the use of Fourier transform

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Summary

Summary & Background

Autoregressive modelling provides a powerful and flexible parametric approach to modelling unior multi-variate time-series data. AR models have mathematical links to linear time-invariant systems, digital filters and Fourier based frequency analyses. A wide range of timedomain and frequency-domain metrics can be readily derived from the fitted autoregressive parameters. These approaches are fundamental in a wide range of science and engineering fields and still undergoing active development. SAILS (Spectral Analysis in Linear Systems) is a python package which implements such methods and provides a basis for both the straightforward fitting of AR models as well as exploration and development of newer methods, such as the decomposition of autoregressive parameters into eigenmodes

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