Nonparametric prediction of non-Gaussian time series

The authors apply the nonparametric kernel predictor to the time-series prediction problem. Because nonparametric prediction makes few assumptions about the underlying time series, it is useful when modeling uncertainties are pervasive, such as when the time series is non-Gaussian. It is shown that...

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Bibliographic Details
Main Authors: Lee, Y.K., Johnson, D.H.
Format: Conference Proceeding
Language:English
Subjects:
Kernel
Marine vehicles
Mean square error methods
Predictive models
Random variables
Speech
State estimation
State-space methods
Uncertainty
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Summary:The authors apply the nonparametric kernel predictor to the time-series prediction problem. Because nonparametric prediction makes few assumptions about the underlying time series, it is useful when modeling uncertainties are pervasive, such as when the time series is non-Gaussian. It is shown that the nonparametric kernel predictor is asymptotically optimal for bounded, mixing time series. Numerical experiments were also performed. For the nonlinear autoregressive process, the kernel predictor is shown to outperform greatly the linear predictor; for the Henon time series, the estimated predictor closely resembles the Henon map.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1993.319699