标题： 通过具有波动性的季节性ARFIMA模型对每日平均PM$_{10}$浓度进行建模和预测 显示英文标题

标题： Modeling and forecasting daily average PM$_{10}$ concentrations by a seasonal ARFIMA model with volatility

摘要： 本文考虑了日平均颗粒物（PM$_{10}$）浓度可能是具有时变方差（波动性）的季节性分数整合过程的可能性。在此背景下，一个方便的扩展是考虑具有GARCH类型创新的SARFIMA模型（Reisen，等，2006a,b）。该模型在理论上是合理的，并通过应用于巴西卡里亚西卡-ES市的PM$_{10}$浓度得到了验证。调整后的模型能够捕捉序列中的动态变化。通过考虑异方差误差，样本外预测区间得到了改进，并能够识别波动性更高的时期。 显示英文摘要

摘要： This paper considers the possibility that the daily average Particulate Matter (PM$_{10}$) concentration is a seasonal fractionally integrated process with time-dependent variance (volatility). In this context, one convenient extension is to consider the SARFIMA model (Reisen, et al, 2006a,b) with GARCH type innovations. The model is theoretically justified and its usefulness is corroborated with the application to PM$_{10}$ concentration in the city of Cariacica-ES (Brazil). The model adjusted was able to capture the dynamics in the series. The out-of-sample forecast intervals were improved by considering heteroscedastic errors and they were able to identify the periods of more volatility.