**Strongly stationary**- If the joint distribution of is the same as the joint distribution of .
**Weakly stationary**- Also called
**second-order stationary**. If the conditions below are satisfied:

Your data sample is , a *realization* of the joint distribution .

This stationary condition is necessary for applying the methods described in this section, since it is required by most of the detailed derivations, especially the Wiener-Khintchine theorem (see 2.4).

Is your time series (weakly) stationary?

- Detrend the data first.
- Think about if the conditions are unchanged for the entire interval when the signals were recorded.
- The change of perturbation amplitude with time does
*not*necessarily mean that your time series is not (weakly) stationary.