Dataset for: Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

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.

在对气候、环境等领域的连续数据开展时间序列分析时，假设该时间序列过程服从高斯分布（Gaussian）的前提往往无法成立。为此，我们提出两种非高斯自回归时间序列模型，此类模型可适配具有偏态与厚尾特征的时间序列数据。我们所提出的两种模型均基于Tukey g-and-h变换（Tukey g-and-h transformation）构建。针对所提模型，我们探讨了参数估计、阶数选择与预测流程，并通过仿真研究验证了模型的性能表现。最后，我们将所提模型应用于两组风速数据集，以此证明模型的实际应用价值。