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From a frequentist perspective, techniques for fitting time series models use a preliminary data-set transformation in order to get stationarity and therefore important information about the dynamic system can be lost.
This article proposes GAMAR for fitting time series data with explanatory variables and autoregressive terms.
Several sensitivity analyses, including the use of robust regressions and of nonparametric methods for fitting time trends and temperature effects in the data, supported these findings.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com