Non-Gaussian autoregressive processes with Tukey g-and-h transformations

Yuan Yan, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


When performing a time series analysis of continuous data, for example, from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey (Formula presented.) -and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Original languageEnglish (US)
Pages (from-to)e2503
Issue number2
StatePublished - May 23 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2640
Acknowledgements: This publication is based upon the work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Grant OSR-2015-CRG4-2640.


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