Symbolic regression of multiple-time-scale dynamical systems

Genetic programming has been successfully used for symbolic regression of time series data in a wide variety of applications. However, previous approaches have not taken into account the presence of multiple-time-scale dynamics despite their prevalence in both natural and artificial dynamical systems. Here, we propose an algorithm that first decomposes data from such systems into components with dynamics at different time scales and then performs symbolic regression separately for each scale. Results show that this divide-and-conquer approach improves the accuracy and efficiency with which genetic programming can be used to reverse-engineer multiple-time-scale dynamical systems.

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