A strong assumption of time series regression, a widely used technique in econometrics, is the stationarity. It requires that the variables entered in the regression have their variances (standard deviations), covariances (auto-correlations), and means, that are independent of time. A stationary series must not wander too far from its mean. In most cases, the assumption is violated (non-stationarity, i.e., random walk) and doing such regression involves what is called a spurious regression. Possible solutions for dealing with this problem is through transformation of the variables.