Experimental approach

We first generated simulated time-series data of length $ N_{t}=128$ from an auto-regressive process of order $ P=2$ with a peaked spectrum. We then computed the DFT, calculated the magnitude-squared of the bins, keeping the $ N=65$ unique bins as our "raw data" $ {\bf x}$. We then created the $ 65\times 3$ matrix $ {\bf A}$ that computed the first three auto-correlation lags (0 through 2), then computed the feature (5.1).



Baggenstoss 2017-05-19