http://arxiv.org/abs/2001.08314
A broad and widely used class of stationary, linear, additive time series models can have statistical properties which many authors have asserted imply that the underlying process must be non-linear, non-stationary, multiplicative, or inconsistent with shot noise. This result is demonstrated with exact and numerical evaluation of the model flux distribution function and dependence of flux standard deviation on mean flux (here and in the literature called the \emph{rms-flux relation}). These models can: (1) exhibit normal, log-normal or other flux distributions; (2) show linear or slightly non-linear rms-mean flux dependencies; as well as (3) match arbitrary second order statistics of the time series data. Accordingly the above assertions cannot be made on the basis of statistical time series analysis alone. Also discussed are ambiguities in the meaning of terms relevant to this study — \emph{linear}, \emph{stationary} and \emph{multiplicative} — and functions that can transform observed fluxes to a normal distribution as well or better than the logarithm.
J. Scargle
Fri, 24 Jan 20
44/72
Comments: 28 paged, 6 figures; submitted to the Astrophysical Journal
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