This paper deals with the k-factor extension of the long memory Gegenbauer process proposed by Gray et al. (1989). We give the analytic expression of the prediction function derived from this long memory process and provide the h-step-ahead prediction error when parameters are either known or estimated. We investigate the predictive ability of the k-factor Gegenbauer model on real data of urban transport traffic in the Paris area, in comparison with other short- and long-memory models.
Publié le : 2001-12-05
Classification:
long memory,
k-factor Gegenbauer process,
prediction function,
prediction error,
urban transport traffic,
[SHS.ECO]Humanities and Social Sciences/Economies and finances,
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR],
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
@article{halshs-00193667,
author = {Ferrara, Laurent and Guegan, Dominique},
title = {Forecasting with k-factor Gegenbauer Processes: Theory and Applications},
journal = {HAL},
volume = {2001},
number = {0},
year = {2001},
language = {en},
url = {http://dml.mathdoc.fr/item/halshs-00193667}
}
Ferrara, Laurent; Guegan, Dominique. Forecasting with k-factor Gegenbauer Processes: Theory and Applications. HAL, Tome 2001 (2001) no. 0, . http://gdmltest.u-ga.fr/item/halshs-00193667/