Nonlinear prediction of chaotic time series. (English) Zbl 0671.62099
Summary: Numerical techniques are presented for constructing nonlinear predictive models directly from time series data. The accuracy of the short-term predictions is tested using computer-generated time series, and comparisons are made of the effectiveness of the various techniques. Scaling laws are developed which describe the data requirements for reliable predictions. It is also shown how to use the models to convincingly distinguish low-dimensional chaos from randomness, and to make statistical long-term predictions.
MSC:
| 62M20 | Inference from stochastic processes and prediction |
| 65C99 | Probabilistic methods, stochastic differential equations |
Keywords:
nonlinear predictive models; short-term predictions; computer-generated time series; Scaling laws; low-dimensional chaos; randomness; long-term predictionsReferences:
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