  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.
