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Nonparametric forecasting in time series: a comparative study

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http://hdl.handle.net/2183/861
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  • Investigación (FIC) [1728]
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Título
Nonparametric forecasting in time series: a comparative study
Autor(es)
Vilar, Juan M.
Cao, Ricardo
Data
2007
Cita bibliográfica
Communications in statistics: simulation and computation, vol 36, n. 2, pp. 311-334.
Resumo
The problem of predicting a future value of a time series is considered in this paper. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables; smoothing parameter selection and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behaviour of the methods is also illustrated by a detailed analysis of two data sets.
Palabras chave
Box-Jenkins
Bootstrap
Dependent data
Kernel regression estimation
Local linear estimation
 
Versión do editor
http://www.informaworld.com/openurl?genre=article&issn=0361-0918&volume=36&issue=2&spage=311;
Dereitos
This is an electronic version of an article published in Communications in statistics, simulation and computation, vol. 36, n.2. Communications in statistics, simulations and computation is available online at: http://www.informaworld.com/
ISSN
0361-0918

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