Modeling double scroll time series
Alexis Dimitriadis, Andrew M. Fraser · IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing · 1993
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.>