We address the issue of recognizing determinism in a time series. Specifically, we employ the method of singular-value decomposition (SVD) to derive the eigenvalue spectra of the trajectory matrices constructed from a number of scalar time series, mainly white noise and chaotic signals, where a very large embedding dimension is used. The results suggest that the SVD eigenvalue spectrum can be employed as a measure of determinism and an estimate for the strength of a noise contained in the time series can be deduced. [References: 18]
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