Dynamic mode decomposition for analysis of time-series data

Since its publication in 2010, the paper by Schmid ( J. Fluid Mech. , vol. 656, 2010, pp. 5–28) has wielded considerable influence, an impact we examine here. That seminal work introduced dynamic mode decomposition, a method for performing flow-field spectral analysis of snapshot sequences of data. As a data-driven approach aimed at uncovering spatial and temporal patterns or modes within datasets, its applicability has extended far beyond fluid mechanics, reaching into a wide array of fields.