Predicting a Critical Transition from Time-series Datasets Generated by LTspice Using a Parameter Space Estimation

This paper shows the prediction of a critical transition from time-series data generated by LTspice, employing parameter space estimation. By conceptualizing the critical transition as a bifurcation phenomenon, we can predict the critical transition by constructing a bifurcation diagram utilizing time-series datasets procured prior to the transition. The target dataset, comprised of time-series data sets, is generated by LTspice whose electronic circuit represents the critical transition inherent in a vegetation biomass model. Numerical experiments show the capability to predict the critical transition by estimating a parameter space only from the generated datasets. In addition, we compare results detected using an early warning signal and predicted using the parameter space estimation.