论文标题
趋势和差异自适应贝叶斯变更点分析和本地异常值评分
Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring
论文作者
论文摘要
我们可以在贝叶斯动态线性模型中适应更改点和局部离群过程，该过程在新型模型中，在新型模型中，我们称为自适应贝叶斯变更点，具有离群值（ABCO）。我们利用一种状态空间方法在存在异常值的情况下识别动态信号以及随机波动性的测量误差。我们发现，对于大多数真实应用程序，全局状态方程参数不足，我们包括在每个时间步中跟踪噪声的本地参数。该设置提供了一个灵活的框架，以检测复杂序列中未指定的更改点，例如局部趋势中有大量中断的框架，对异常值和异性噪声噪声具有稳健性。最后，我们将算法与几种替代方案进行了比较，以证明其在不同的模拟方案和关于美国经济的两个经验例子中的功效。
We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends, with robustness to outliers and heteroskedastic noise. Finally, we compare our algorithm against several alternatives to demonstrate its efficacy in diverse simulation scenarios and two empirical examples on the U.S. economy.
