Abstract
The False Nearest Neighbors (FNN) method is particularly relevant in several fields of science and engineering (medicine, economics, oceanography, biological systems, etc.). In some of these applications, it is important to give results within a reasonable time scale; hence, the execution time of the FNN method has to be reduced. This chapter1 introduces the basic theory and concepts of nonlinear dynamics and chaos, and then describes some parallel implementations of the FNN method for distributed, shared and hybrid memory architectures. The accuracy and performance of the parallel approaches are then assessed and compared with the best sequential implementation of the FNN method, which appears in the TISEAN project.
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