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

Yuan Yan and Marc G. Genton

Environmetrics, 2019, vol. 30, issue 2

Abstract: 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 g‐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.

Date: 2019
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