This paper considers the modeling and forecasting of daily maximum hourly ozone concentrations in Laranjeiras, Serra, Brazil, through dynamic regression models. In order to take into account the natural skewness and heavy-tailness of the data, a linear regression model with autoregressive errors and innovations following a member of the family of scale mixture of skew-normal distributions was considered. Pollutants and meteorological variables were considered as predictors, along with some deterministic factors, namely week-days and seasons. The Oceanic Niño Index was also considered as a predictor. The estimated model was able to explain satisfactorily well the correlation structure of the ozone time series. An out-of-sample forecast study was also performed. The skew-normal and skew-t models displayed quite competitive point forecasts compared to the similar model with gaussian innovations. On the other hand, in terms of forecast intervals, the skewed models presented much better performance with more accurate prediction intervals. These findings were empirically corroborated by a forecast Monte Carlo experiment.

中文翻译：

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使用带有偏斜和重尾创新的RegAR模型对每日最大每小时臭氧浓度进行建模和预测

本文考虑通过动态回归模型对巴西塞拉拉兰杰拉斯的每日最大每小时臭氧浓度进行建模和预测。为了考虑到数据的自然偏度和重尾性，考虑了线性正态分布的比例混合族成员中具有自回归误差和创新的线性回归模型。污染物和气象变量与一些确定性因素（即工作日和季节）一起被视为预测因素。大洋Niño指数也被视为预测指标。估计的模型能够令人满意地解释臭氧时间序列的相关结构。还进行了样本外预测研究。与具有高斯创新的类似模型相比，偏态正态和偏态t模型显示出相当有竞争力的积分预测。另一方面，在预测间隔方面，偏斜模型表现出更好的性能以及更准确的预测间隔。这些发现在经验上得到了蒙特卡洛预报实验的证实。