Consider, as an illustrative example, a winding pipeline running underground (Figure ). We aim to measure the state of the liquid flowing inside. Above ground, it is more practical and cost-effective to place measurement sensors in an orderly, Euclidean grid and mark them by GPS coordinates rather than align them along the path of the pipe, which is not immediately visible. Consequently, our sensors, and therefore our measurements, appear scrambled along the "true" 1D spatial dimension (the pipeline path). Our dimension in the parameter space might also appear scrambled: we may wish to use historical data with many samples at the same measurement locations -but at the time when the data was collected, we were not prudent enough to label them with the right operating conditions. The first (left) component of Figure illustrates such a winding pipe as well as a series of resulting shuffled datasets, with the time dimension also shuffled.