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
Summary
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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have