Is a model with a sine wave time-series stationary? Today I was introduced to the definition of stationarity being that the marginal distribution of a process does not change over time, and the mean and variance remain constant over time. I questioned whether a process with a perfect sine-wave with an unknown starting point and unknown period would be stationary, as it is purely deterministic however the mean seems to change with time. 
Asking my professor following class, his answer was that such a process would indeed be stationary, despite the mean changing. However on this answer I found on CV, the mean changing is cited as a reason for such a process being non-stationary. Now I'm a little bit confused about the definition of a stationary process, and still unsure about the stationarity of a sine wave. 
Am I missing some point here? Or is there varying definitions for a stationary process?
 A: Stationarity is a property of a stochastic process. A perfect sine wave is not a stochastic process. Hence, it can't be stationary or non-stationary. It doesn't have any random parts.
$$y_t=\sin (\phi t+\theta)$$
It's like asking whether a song is black or white. The music has no color, it has many other properties but color is not one of them. 
Now, you could look at the problem differently. As you wrote the phase and frequency are unknown. So, if you look at the family of processes:
$$y_t=\sin (\phi_i t+\theta_i)$$
Where $\phi_i,\theta_i$ come from some distribution, and you're to estimate $E[y_t]$, then it's a more interesting question. It's still not a stochastic process though.
The stochastic process represents an evolution of random variables. In the case of a perfect sine wave it's entirely defined by two random variables $\phi_i,\theta_i$. There's no evolution.
In other words there's got to be some kind of randomness and uncertainty introduced as time progresses in order for the process to be stochastic. In your case all the uncertainty is introduced at time 0.
A: For the sine wave to be stationary it needs a random phase!
As whuber points out, it is not enough that the phase is random, it must have a uniform distribution on the interval $[0,2\pi)$.
