Time series analysis if it is predictable or not I have two types of time series, stationary time series dataset and non stationary time series dataset. Now I need to know if these two series predictable or not. I read about the models of predictability and the measure of predictability like mean ,RMSE,...
But really I am very confuse because I do not know how to know if the series is predictable or not.

Both are predictable, just not equally well. Consider a simple nonstationary random walk
$$
y_t=y_{t-1}+\epsilon_t=y_0+\sum_{j=1}^t\epsilon_j
$$
Suppose we observe this process until $T$ and want to forecast $h$ periods ahead. Write
$$
y_{T+h}=y_T+\sum_{j=1}^{T+h}\epsilon_j
$$
Then, since the errors are mean zero, our best forecast, for any horizon $h$, simply is $y_T$, the last observed value. In that sense, the process is "predictable". However, the forecast error variance $Var(\sum_{j=1}^{T+h}\epsilon_j)=h\sigma^2$ will increase without bound in $h$, which is not generally the case for a stationary process.
