Time series prediction by Perturbed Fuzzy Model
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abstract
This paper presents a fuzzy system
approach to the prediction of nonlinear
time series and dynamical systems based
on a fuzzy model that includes its
derivative information. The underlying
mechanism governing the time series,
expressed as a set of IF–THEN rules, is
discovered by a modified structure of fuzzy
system in order to capture the temporal
series and its temporal derivative information.
The task of predicting the future is
carried out by a fuzzy predictor on the
basis of the extracted rules and by the
Taylor ODE solver method. We have
applied the approach to the benchmark
Mackey-Glass chaotic time series.
