Distances Between Finite-Horizon Linear Behaviors

The paper introduces a class of distances for linear behaviors over finite time horizons. These distances allow for comparisons between finite-horizon linear behaviors represented by matrices of possibly different dimensions. They remain invariant under coordinate changes, rotations, and permutations, ensuring independence from input-output partitions. Moreover, they naturally encode complexity-misfit trade-offs for LTI behaviors, providing a principled solution to a longstanding puzzle in behavioral systems theory. The resulting framework characterizes modeling as a minimum distance problem, identifying the MPUM as optimal among all systems unfalsified by a given dataset. Finally, we illustrate the value of these metrics in a time-series anomaly detection task, where their finer resolution yields superior performance over existing distances.