Suppose that I'm given a sample from time-series $(x\_n)\_{n=1}^N$ and want to decide if it comes from an OU process or not. Is there a (rigorous) test I can use?


So far, everything I've seen is hand-wavy...



Test for OU-Process
$\newcommand\th{\theta}$ $\newcommand\Si{\Sigma}$
The Ornstein--Uhlenbeck (OU) process is a Gaussian process, with the mean and covariance functions being [known functions](https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process#Mathematical_properties) depending on $s\le4$ unknown real-valued parameters of the OU process. So, for the sample $X:=(x\_1,\dots,x\_n)$, we know the functions $\mu$ and $\Si$ given by $\mu(\th):=E\_\th X$ and $\Si(\th):=Cov\_\th X$ for all $s$-tuples $\th$ of the real-valued parameters. Then 
$$Z(\th):=\Si(\th)^{-1/2}(X-\mu(\th))$$
has the standard normal distribution in $\mathbb R^n$. 


Now using appropriate estimates of $\th$ and partitioning $\mathbb R^n$ into some number $k>s+1$ of disjoint Lebesgue-measurable sets of nonzero (say the same) Gaussian measure, we can use e.g. a chi-square goodness-of-fit test, with a test statistic distributed approximately as $\chi^2$ with $k-s-1$ degrees of freedom, as described e.g. by [Watson](https://www.jstor.org/stable/2983906). 




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Another goodness-of-fit test, based on a strict negative definiteness of Euclidean distance, is described in Section 3 of the paper by [Székely and Rizzo](http://personal.bgsu.edu/~mrizzo/energy/MVN-GOF-2005.pdf). 


