Stationarity/ White noise

In general, we need stationary time series in order to build statistical models.

That means there should be no systematic change in the mean/variance of the data.
Also, the data should have no seasonality (periodic fluctuations).

However, most financial/economic data is non-stationary. That means there is some trend (systematic change) or seasonality.
Therefore we need to transform this data in order to make it stationary.

White noise

A dataset has white noise if the mean is 0, the variance is constant and the lags have 0 autocorrelations. Basically one can think of white noise as a dataset of random numbers. As the autocorrelation at all lags is 0 it is not possible to forecast such data based on its past values.