This question is related to a normal score transformation which is performed in the paper by RUAN, Quansong, et al. Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics, 2006, 22.20: 2532-2538.
They claim that given a variable X with observations $x= (x_1,\ldots,x_n)$ that are not normally distributed, normal score transformation is performed by first creating a rank vector observations $x$, $R^x = (R_1^x,\ldots, R_n^x)$ and then using and then transforming to $x'=(x_1',\ldots,x_n)$, by $x_i\ = \phi_{-1} (\frac{R_i^x}{n+1} )$ where $\phi_{-1}$ is the inverse of cummulative normal distribution.
It's intuitively clear (but maybe a bit harder to prove?) that resulting variable $X'$ has observations normally distributed, but I have a question about time series dependence. If we had that observations $x$ were correlated, say, over 4 adjacent observations, , i.e. $x_{n+1} = f(x_{n},x_{n-1},x_{n-2})$, how does this change after the normal transformation? Can we still find a function g, so that $x'_{n+1} = g(x'_{n},x'_{n-1},x'_{n-2})$ would hold? Since we're transforming based on the rank of data, I expect this to be not possible?