Nonparametric forecasting in time series: a comparative study
Ver/Abrir
Use este enlace para citar
http://hdl.handle.net/2183/861Colecciones
- GI-MODES - Artigos [139]
Metadatos
Mostrar el registro completo del ítemTítulo
Nonparametric forecasting in time series: a comparative studyFecha
2007Cita bibliográfica
Communications in statistics: simulation and computation, vol 36, n. 2, pp. 311-334.
Resumen
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 clave
Box-Jenkins
Bootstrap
Dependent data
Kernel regression estimation
Local linear estimation
Bootstrap
Dependent data
Kernel regression estimation
Local linear estimation
Versión del editor
Derechos
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