We introduce harmonic cross-correlation decomposition (HCD) as a tool to detect and visualize features in the frequency structure of multivariate time series. HCD decomposes series into spatiotemporal modes with leading representing dominant oscillatory patterns data. is closely related data-adaptive (DAHD) [Chekroun Kondrashov, Chaos 27, 093110 (2017)] that it performs an eigendecomposition gr...

Multivariate time series exhibit two types of dependence: across variables and points. Vine copulas are graphical models for the dependence can conveniently capture both in same model. We derive maximal class graph structures that guarantee stationarity under a natural verifiable condition called translation invariance. propose computationally efficient methods estimation, simulation, predictio...