# Help with understanding stationarity

I'm new in time series and the concept of stationarity has been bugging me for a while :( I know the definition of stationarity but it is not 100% clear for me why for example we have to difference the data in order to do time series analysis.

Could someone give me a very simple and short example of time series analysis where the data is non-stationary (for example regression of some kind) and show the procedures we must do in order to get a good predictions.

This is how I understand the reason of differencing so far:

Data is not stationary --> Difference the data (e.g. $\Delta x_t = x_t - x_{t-1}$) --> Make the model for differenced data $\Delta x_t$ and solve the coefficients for the model --> make predictions $\widehat{x}_{t+m}$ with model --> Undifference the predicted data to get back to the original scale $\Delta^{-1}\widehat{x}_{t+m}$.

Is my interpretation correct or not? Thank you for any help =) Hope my question isn't unclear. The reason I posted this question because I haven't found good enough examples to show me the procedures in detail =(

• "why ... we have to difference the data in order to do time series analysis" -- in fact we only need to do that if that would make non-stationary data stationary. If it's already stationary, there's nothing to do. – Glen_b Mar 28 '13 at 6:57