# Stationarity and White noise

#### zeke512

##### New Member
Hi all,
I'm just beginning to learn about time series analysis for my final year project at uni and I am confused about stationarity.
A time series has to be stationary to be able to apply an ARMA model, however surely if a time series is stationary it is a white noise process and therefore cannot be predicted. Can anyone please point out what I am missing here?

Thank you,
Zeke

#### GretaGarbo

##### Human
No, an AR(1) is stationary if the absolute value of "a" is less than 1.

X_t = a*X_t-1 + epsilon_t

#### zeke512

##### New Member
Ok, thanks but if X_t is stationary, it must have constant E(x_t)=mean and constant variance and therefore surely it must be white noise and no forecasting can be achieved because it is random data with no trend? Really struggling to understand here, I'm fairly new to stats and I'm trying to work out what basics I need to understand to apply time-series forecasting and possibly work out a model for a forecasting task. I'm supposed to be doing a literature review now looking at modelling methods for time-series'