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Nonparametric forecasting in time series: a comparative study
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.Galicia. Consellería de Innovación, Industria e Comercio; PGIDT01PXI10505PRGalicia. Consellería de Innovación, Industria e Comercio; PGIDT03PXIC10505PNMinisterio de Ciencia y Tecnología; BMF2002-0026
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