BAYESIAN OUTLIER DETECTION IN NON-GAUSSIAN AUTOREGRESSIVE TIME SERIES

Silva Maria Eduarda; Pereira Isabel; McCabe Brendan

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BAYESIAN OUTLIER DETECTION IN NON-GAUSSIAN AUTOREGRESSIVE TIME SERIES

机译：非高斯自回归时间序列中的贝叶斯外在检测

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摘要

This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.

机译：这项工作研究了卷积闭参数族中具有余量的非高斯自回归时间序列模型中的离群值检测和建模。该框架允许用于计数和正数据类型的多种模型。本文研究了未加入过程动态过程但其存在可能对基于数据的统计推断产生不利影响的加法异常值。这里提出的贝叶斯方法允许人们在每个时间点估计异常值发生的概率及其相应的大小，从而确定需要进一步研究的观测值。使用模拟和观察到的数据集说明了该方法。

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来源
《Journal of Time Series Analysis》 |2019年第5期|631-648|共18页
作者
Silva Maria Eduarda; Pereira Isabel; McCabe Brendan;
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作者单位

Univ Porto, Fac Econ, Rua Dr Roberto Frias S-N, P-4200464 Porto, Portugal|CIDMA, Aveiro, Portugal;

CIDMA, Aveiro, Portugal|Univ Aveiro, Dept Matemat, Aveiro, Portugal;

Univ Liverpool, Management Sch, Liverpool, Merseyside, England;

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正文语种 eng
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Convolution closed infinitely divisible models; additive outliers; Bayesian framework; MCMC; time series of counts; state space models;

机译：卷积封闭无限可分隔的模型;添加剂异常值;贝叶斯框架;MCMC;时间序列计数;状态空间模型;

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专利

1. BAYESIAN OUTLIER DETECTION IN NON-GAUSSIAN AUTOREGRESSIVE TIME SERIES [J] . Silva Maria Eduarda, Pereira Isabel, McCabe Brendan Journal of Time Series Analysis . 2019,第5期

机译：非高斯自回归时间序列中的贝叶斯异常检测
2. Combining Bayesian method and Kalman smoother for detection additive outlier patches in autoregressive time series [J] . Mohammadinia Farideh, Chinipardaz Rahim Communications in Statistics . 2019,第6a7期

机译：结合贝叶斯方法和卡尔曼平滑器来检测自回归时间序列中的附加离群点
3. Combining Bayesian method and Kalman smoother for detection additive outlier patches in autoregressive time series [J] . Mohammadinia Farideh, Chinipardaz Rahim Communications in Statistics . 2019,第6a7期

机译：结合贝叶斯方法和卡尔曼更顺畅，用于自回归时间序列中的检测添加剂异常补丁
4. A hierarchical Bayesian learning framework for autoregressive neural network modeling of time series [C] . Acernese F., De Rosa R., Milano L., Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on . 2003

机译：时间序列自回归神经网络建模的分层贝叶斯学习框架
5. Detection of outliers in time series data. [D] . Kiware, Samson. 2010

机译：检测时间序列数据中的异常值。
6. Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya [O] . Raymond Nyoka, Thomas N. O. Achia, Jimmy Omony, 1944

机译：时间序列非高斯贝叶斯二元模型应用于HMPV和RSV数据：肯尼亚Dadaab案
7. Detection of outlier patches in autoregressive time series [O] . Justel A., Peña Daniel, Tsay Ruey S. 1998

机译：检测自回归时间序列中的异常值补丁
8. Linear Models, TIme Series and Outliers, 5: Outliers in Time Series. [R] . Abraham, B., Box, G. E. P. 1975

机译：线性模型，时间序列和异常值，5：时间序列中的异常值。

BAYESIAN OUTLIER DETECTION IN NON-GAUSSIAN AUTOREGRESSIVE TIME SERIES

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