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Forecasting with k ‐factor Gegenbauer Processes: Theory and Applications
Author(s) -
Ferrara L.,
Guégan D.
Publication year - 2001
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.815
Subject(s) - factor (programming language) , gray (unit) , computer science , mathematics , function (biology) , long memory , statistical physics , econometrics , algorithm , physics , medicine , volatility (finance) , evolutionary biology , biology , radiology , programming language
Abstract 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. Copyright © 2001 John Wiley & Sons, Ltd.