A functional wavelet-kernel approach for time series prediction

We consider the prediction problem of a time series on a whole time interval in terms of its past. The approach that we adopt is based on functional kernel nonparametric regression estimation techniques where observations are discrete recordings of segments of an underlying stochastic process consid...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2006-11, Vol.68 (5), p.837-857
Hauptverfasser:	Antoniadis, Anestis, Paparoditis, Efstathios, Sapatinas, Theofanis
Format:	Artikel
Sprache:	eng
Schlagworte:	Besov spaces Coefficients Data smoothing Econometrics Estimation Exact sciences and technology Exponential smoothing Forecasting models Fourier analysis Functional kernel regression Inference from stochastic processes time series analysis Mathematics Modeling Musical intervals Nonparametric inference Numerical analysis Numerical analysis. Scientific computation Pointwise prediction intervals Probability and statistics Regression analysis Resampling Sciences and techniques of general use Seasonal autoregressive integrated moving average models Smoothing splines Statistical methods Statistics Stochastic models Stochastic processes Studies Time series Time series forecasting Time series models Time series prediction Wavelet analysis Wavelet transforms Wavelets α-mixing
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