In most application cases your observed $Y_t$ is nonstationary but you can still model it using the generalized form of ARIMA such as SARIMA. If your $f(\beta,t)$ is simply a linear function of $t$ and the noise part $e_t$ is assumed to follow an ARMA process, then you can difference once your time series to model $Y_t$ as a ARIMA(p,d=1,q) model where d= is the order of difference. If $f(\beta,t)$ is a general polynomial trend then you can difference more times to arrive at a stationary ARMA model as shown in Brockwell et al's Introduction to Time Series and Forecasting. If $f(\beta,t)$ also contains seasonal trend then you can additionally do seasonal difference to arrive at SARIMA model which is a more generalized form of ARIMA and can account for both autoregressive lagged difference and seasonal difference mentioned above.
Of course In most applications you cannot even be sure your noise is really generated by a (stationary) ARMA process (white noise included since it's a special case of ARMA), albeit it's a common assumption in applications such as un-systematic measurement error or consecutive daily temperature series.