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.
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.
| Original language | English |
|---|---|
| Article number | 1982 |
| Pages (from-to) | 1-6 |
| Number of pages | 6 |
| Journal | Journal of open source software |
| Volume | 5 |
| Issue number | 47 |
| DOIs | |
| Publication status | Published - 6 Mar 2020 |
Bibliographical note
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