# White noise ACF - PACF

I found PACF and ACF like the following table . But, how can I decide whether there exists white noise? And what is white noise? If there is no white noise, can I say being stationary?

• These are all pretty elementary questions and are normally covered in introductory time series courses. Have you tried looking them up in a textbook? If you have and still do not understand, please specify in more detail what is unclear. – Richard Hardy Mar 2 '15 at 19:09
• i only know that i need to look at first two blocks to decide white noise. @RichardHardy – B11b Mar 2 '15 at 19:38
• but i dont know how to decide. this point is unclear @RichardHardy – B11b Mar 2 '15 at 19:39
• The bars at lag 1 and lag 4 in both ACF and PACF plots stick out quit a lot beyond the confidence bound (the dashed line). The confidence bound is defined as follows. There is only 5% probability that the bar would stick out beyond the bound if the underlying data generating process had zero ACF/PACF. Hence, it is quite unlikely (only 5% chance) that the underlying process has zero ACF/PACF. That is, it is quite likely (95% chance) that the ACF/PACF is real and not just a matter of coincidence. That is, there is almost surely a pattern in the data $\rightarrow$ your data is not white noise. – Richard Hardy Mar 2 '15 at 19:50
• okay thank you so much:). what i want to learn is that! @RichardHardy – B11b Mar 2 '15 at 19:52

How do I know whether it is white noise? A general assumption is that if 95% of the spikes in the Auto-correlation Function lie within (+/-)2/sqrt(T), where T being the length of the time series.