Bayesian nonparametric change point detection for multivariate time series with missing observations

Corradin, Riccardo; Danese, Luca; Ongaro, Andrea

doi:10.1016/j.ijar.2021.12.019

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…Dubey et al [10] studied the online change point detection problem in the network where the pattern of the missing data of the network is heterogeneous. Corradin et al [7] studied multiple change-point detections in multivariate time series with missing values. The conditional distribution of each data point given the rest data is derived to handle the missing value problem.…”

Section: Change Point Detection For Partially Observed Data With Samp...mentioning

confidence: 99%

“…By plugging in the weighted posterior distribution of P (p k |X k,1 , • • • , X k,T ) into the UCB reward function in (7), we will derive the objective function to be optimized and propose an integer programming algorithm to optimize the number of test kits c k,T +1 in each region k at time T + 1. Proposition 4.4.…”

Section: Optimization Algorithm For Planningmentioning

confidence: 99%

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Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Mei

Holte

et al. 2022

Journal of Applied Statistics

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In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.

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Section: Change Point Detection For Partially Observed Data With Samp...mentioning

confidence: 99%

Section: Optimization Algorithm For Planningmentioning

confidence: 99%

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Mei

Holte

et al. 2022

Journal of Applied Statistics

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“…Shi et al (2022) constructed a single change-point test based on the covariance, cumulative sum and likelihood ratio of forecast residuals to detect the potential change point in time series. Corradin et al (2022) constructed a Bayesian nonparametric multivariate change-point detection method by combining prior distributions with multivariate kernels and argued that the posterior probability of most change points should be lower than the posterior estimate. Xie et al (2022) calculated the fitted local trend line based on the piecewise linear representation algorithm and the Akaike information criterion to realize change-point detection and series division and classified change points into three categories with the help of the slope and intercept.…”

Section: Introductionmentioning

confidence: 99%

Study on a mother wavelet optimization framework based on change-point detection of hydrological time series

Huang

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological time series (HTS) are the key basis of water conservancy project planning and construction. However, under the influence of climate change, human activities and other factors, the consistency of HTS has been destroyed and cannot meet the requirements of mathematical statistics. Series division and wavelet transform are effective methods to reuse and analyse HTS. However, they are limited by the change-point detection and mother wavelet (MWT) selection and are difficult to apply and promote in practice. To address these issues, we constructed a potential change-point set based on a cumulative anomaly method, the Mann–Kendall test and wavelet change-point detection. Then, the degree of change before and after the potential change point was calculated with the Kolmogorov–Smirnov test, and the change-point detection criteria were proposed. Finally, the optimization framework was proposed according to the detection accuracy of MWT, and continuous wavelet transform was used to analyse HTS evolution. We used Pingshan station and Yichang station on the Yangtze River as study cases. The results show that (1) change-point detection criteria can quickly locate potential change points, determine the change trajectory and complete the division of HTS and that (2) MWT optimal framework can select the MWT that conforms to HTS characteristics and ensure the accuracy and uniqueness of the transformation. This study analyses the HTS evolution and provides a better basis for hydrological and hydraulic calculation, which will improve design flood estimation and operation scheme preparation.

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“…Binary segmentation algorithm or its variants are suggested for the detection of multiple changepoints. Corradin et al (2022) considered a multiple changepoint detection model for a multivariate time series, working in a Bayesian nonparametric framework and allowing missing observations imputed according to their full conditionals within a Markov chain Monte Carlo (MCMC) strategy.…”

Section: Introductionmentioning

confidence: 99%

Bayesian multiple changepoint detection with missing data and its application to the magnitude‐frequency distributions

Shaochuan

2022

Environmetrics

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The detection of abrupt changes in an evolving pattern of time series in the presence of missing data still poses a challenge to real applications. We formulate the multiple changepoint problem into a latent Markov model on a countably infinite state space. For efficiency-enhancing, we propose a partially collapsed Gibbs sampler for the inference of the joint posterior of the number of changepoints and their locations. Variants of Viterbi algorithms are suggested for obtaining the MAP estimates of random changepoints in the presence of missing data, which provides better performances in these varying-dimensional problems. The method is generally applicable for multiple changepoint detection under a variety of missing data mechanism. The method is applied to a case study of the magnitude-frequency distribution of the 2010 Darfield M7.1 earthquake sequence in New Zealand. We find out some unusual features of the seismic b-value in the Darfield earthquake sequence. It is noted that two changepoints are detected and in contrast to the background seismic b-value, relatively low b-values in the early aftershock propagation period are identified. We suggest that this might be a forewarning of potentially devastatingly strong aftershocks.The advance in the method of b-value changepoint detection will enhance our understanding of earthquake occurrence and potentially lead to improved risk forecasting.

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Bayesian nonparametric change point detection for multivariate time series with missing observations

Cited by 8 publications

References 37 publications

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Study on a mother wavelet optimization framework based on change-point detection of hydrological time series

Bayesian multiple changepoint detection with missing data and its application to the magnitude‐frequency distributions

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