Constrained Randomization of Time Series Data

  title={Constrained Randomization of Time Series Data},
  author={Thomas Schreiber},
  journal={Physical Review Letters},
  • T. Schreiber
  • Published 9 March 1998
  • Computer Science, Physics
  • Physical Review Letters
A new method is introduced to create artificial time sequences that fulfil given constraints but are random otherwise. Constraints are usually derived from a measured signal for which surrogate data are to be generated. They are fulfilled by minimizing a suitable cost function using simulated annealing. A wide variety of structures can be imposed on the surrogate series, including multivariate, nonlinear, and nonstationary properties. When the linear correlation structure is to be preserved… 
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There are at most finitely many solutions ͕x 0 n ͖, including the original sequence and the time reversed sequence ͕x N2n21 ͖. However, we will not usually find the absolute minimum