Bosq, Denis Nonparametric prediction for unbounded almost stationary processes. (English) Zbl 0768.62083 Nonparametric functional estimation and related topics, NATO ASI Ser., Ser. C 335, 389-403 (1991). [For the entire collection see Zbl 0722.00032.]A class of nonparametric predictors for unbounded mixing processes based on regression estimates, are studied. This class contains the classical convolution kernel and orthogonal series predictors. It is shown that it is not necessary to remove seasonal fluctuations before using the nonparametric prediction method. Finally, the author deals with the practical use of the method and compares it with the Box-Jenkins method. Reviewer: M.P.Moklyachuk (Kiev) Cited in 1 Document MSC: 62M20 Inference from stochastic processes and prediction 62G07 Density estimation Keywords:almost stationary processes; density estimation; regression estimation; nonparametric predictors; unbounded mixing processes; regression estimates; orthogonal series predictors; seasonal fluctuations; Box- Jenkins method Citations:Zbl 0722.00032 PDFBibTeX XMLCite \textit{D. Bosq}, in: Laws of the iterated logarithm for density estimators. . 389--403 (1991; Zbl 0768.62083)