The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and two real data applications on human papillomavirus incidence in Girona (Catalunya, Spain) and COVID-19 incidence in the Chinese region of Heilongjiang.

翻译：这项工作的主要目的是提出一个新的模型,能够处理可能误报的连续连续时间序列,拟议的模型能够处理连续时间序列数据中的自动关系结构,这些数据可能部分或全部地报告不足或报告过多,其表现表现表现通过全面模拟研究加以说明,该研究考虑了吉罗纳(西班牙卡塔卢尼亚)的几处自动关系结构和关于人类乳头瘤病毒发病率的两个真实数据应用,以及黑龙江中国地区的COVID-19发病率。