Modeling double scroll time series

The ubiquity of strange attractors in nature suggests that nonlinear modeling techniques can improve performance in some signal processing applications. The authors introduce mixed state Markov models (MSMMs), a refinement of hidden filter HMMs, and apply both to a synthetic double scroll time series. Forecasts by HFHMMs diverge after a few steps. Using ad hoc procedures, forecasts by MSMMs, even models generated by crude methods without iterative optimization, can be made more stable