论文标题
时间变化的自动回归型号用于计算时间序列
Time-varying auto-regressive models for count time-series
论文作者
论文摘要
计数值时间序列数据通常在许多应用领域收集。我们特别有动力研究COVID-19引起的每日新病例的计数时间序列。我们提出了两个贝叶斯模型，即计数的时变半参数AR（P）模型，然后考虑到差异的快速变化，然后是时变的Ingarch模型。我们计算拟议的贝叶斯方法相对于平均地狱林指标的后收缩率。我们提出的模型结构适合哈密顿蒙特卡洛（HMC）采样，以进行有效的计算。我们通过与某些接近现有方法相比表现出优势的模拟来证实我们的方法。最后，我们分析了新确认的案件的日常时间序列数据，以通过不同的政府干预来研究其传播。
Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID-19 spread. We propose two Bayesian models, a time-varying semiparametric AR(p) model for count and then a time-varying INGARCH model considering the rapid changes in the spread. We calculate posterior contraction rates of the proposed Bayesian methods with respect to average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the close existing methods. Finally we analyze the daily time series data of newly confirmed cases to study its spread through different government interventions.
