Spectral measure of a stationary time series ($\gamma (k)={\int }_{-\pi }^{\pi }{e}^{ik\omega }dF(\omega )$)

The spectral measure of a weakly stationary process is the finite nonnegative measure whose Fourier coefficients are its autocovariances. Its total mass is the process variance. A time-series spectral density exists exactly when this measure is absolutely continuous with respect to Lebesgue measure.

For uncorrelated zero-mean unit-variance amplitudes $A,B$, the process $A\cos ({\omega }_{0}t)+B\sin ({\omega }_{0}t)$ has covariance $\cos ({\omega }_{0}k)$ and hence the displayed two-atom spectral measure of a stationary time series. Independence or Gaussian amplitudes are unnecessary for weak stationarity. A phase shift need not preserve the whole joint amplitude distribution, so strict stationarity cannot be inferred from these second moments alone.

The cumulative function of the finite positive spectral measure of a stationary time series is nondecreasing and right-continuous, with total mass equal to the variance. For an absolutely continuous measure, $F(\lambda )={\int }_{-\pi }^{\lambda }f(u)du$, where $f$ is the time-series spectral density. Under angular-frequency normalization, ${\gamma }_k={\int }_{-\pi }^{\pi }{e}^{ik\lambda }dF(\lambda )$ and, when the covariance series is absolutely summable, $f(\lambda )=(2\pi {)}^{-1}{\sum }_{k\in \mathbb{Z}}{\gamma }_k{e}^{-ik\lambda }$. Atoms describe persistent periodic components and cannot be represented by an ordinary density.