A method is presented for detection of synchronous signals in multidimensional time series data. It is based on estimation of eigenvalues of spectral matrices and canonical coherences in moving time windows and extraction of an aggregated signal (a scalar signal, which accumulates in its own variations only those spectral components which are present simultaneously in each scalar time series). It is known that an increase in the collective behavior of the components of some systems and an enlarged spatial radius of fluctuations of their parameters could be regarded as an important precursor of an oncoming catastrophe, i.e. abrupt change of the system's parameter values. From that point of view, detection of synchronous signals in various geophysical parameters, measured at points of some network, covering a given area of the Earth's crust, is of interest for identifying precursors of strong earthquakes. Some examples are presented of the use of this technique in the processing of real geophysical time series.
Temporal range exploration of large scale multidimensional time series data
