A Data Science Central Community
from a practitioner's perspective, is that it is a measure of noise, a detector of outliers that may show up as unaccounted-for noise, from the way, say, a process is producing the data, even a data-entry process, or some other force or process/system giving rise to that (those) particular noise(s). Yes?
So the thing would be try various tactics for reducing bumpiness, maybe by screening those outliers, etc., and even running a TSA on the residuals after factoring or "partialing out" the bumpiness.
But isn't that part of whitening? An SOP in Box-Jenkins Analysis or old ARIMA models?
Help me understand this.