nature of output of the addition of white noise with colord noise

I want to know that the addition of a uncorrelated time series x with a correlated time series, say y generates a time series but is that correlated or uncorrelated output? For example: x is White Gaussian noise and y is pink noise, then z = x+y Looking at the plot which is the output of the noisy time series, it is hard to say whether z is correlated or uncorrelated. Is there a rule about the nature of the output? • Can you re-consider your question a little? You begin with x being a correlated time series and y an uncorrelated series, but in the very next sentence, x is white Gaussian noise (which most people would regard as an uncorrelated series) and y is pink noise (which most people would regard as a correlated series). So, which is which? and what, if any, is the cross-correlation between x and y? Sep 1 '20 at 2:03
• @DilipSarwate: sorry for the typo. I have fixed it.
– Sm1
Sep 1 '20 at 2:17

Are $$x$$ and $$y$$ uncorrelated? In this case the autocorrelation function of $$x+y$$ is just the sum of the autocorrelation functions of $$x$$ and $$y$$, which if one of them is not white will lead to its sum also not being white.
In general, for stationary processes you have $$\phi_{zz}(\tau) = \phi_{xx}(\tau) + \phi_{yy}(\tau) + \phi_{xy}(\tau) + \phi_{yx}(\tau)$$ where $$\phi_{xy}(\tau) = \mathbb{E}\{x(t)y(t+\tau)\}$$. Moreover, since the process is stationary you also have $$\phi_{xy}(\tau) = \phi_{xy}(-\tau) = \phi_{yx}(\tau)$$. So in general, the autocorrelation of $$z$$ depends not only on the autocorrelations of $$x$$ and $$y$$ but also their cross correlations.
• It does answer your question. If your variables are independent, the correlation of the sum is equal to the sum of the correlations. Therefore, if one is not white (your $y$), the sum $x+y$ is also not white (i.e., samples are correlated to each other correlated), as I wrote in my second sentence. Sep 1 '20 at 13:59