The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints

Nam, Christopher F. H.; Aston, John A. D.; Eckley, Idris A.; Killick, Rebecca

doi:10.1080/00401706.2014.902776

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Changepoint detection is an area of research with immediate practical applications in the monitoring of financial data (Bai and Perron 1998;Frick et al 2014), network traffic data (Lévy-Leduc and Roueff 2009;Lung-Yut-Fong et al 2012), as well as bioinformatics (Maidstone et al 2017;Guédon 2013), environmental (Nam et al 2015) and signal or speech processing applications (Desobry et al 2005;Haynes et al 2017). For instance, a sudden change in (mean) activity of one or more data streams in a network could hint at an intruder sending data to an unknown host from several infected computers.…”

Section: Introductionmentioning

confidence: 99%

BayesProject: Fast computation of a projection direction for multivariate changepoint detection

2020

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This article focuses on the challenging problem of efficiently detecting changes in mean within multivariate data sequences. Multivariate changepoints can be detected by projecting a multivariate series to a univariate one using a suitable projection direction that preserves a maximal proportion of signal information. However, for some existing approaches the computation of such a projection direction can scale unfavourably with the number of series and might rely on additional assumptions on the data sequences, thus limiting their generality. We introduce BayesProject, a computationally inexpensive Bayesian approach to compute a projection direction in such a setting. The proposed approach allows the incorporation of prior knowledge of the changepoint scenario, when such information is available, which can help to increase the accuracy of the method. A simulation study shows that BayesProject is robust, yields projections close to the oracle projection direction and, moreover, that its accuracy in detecting changepoints is comparable to, or better than, existing algorithms while scaling linearly with the number of series.

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Section: Introductionmentioning

confidence: 99%

BayesProject: Fast computation of a projection direction for multivariate changepoint detection

2020

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“…These methods are of fundamental importance in many areas, including econometrics (Aue et al, 2012;Hlávka et al, 2017), medicine (Fried and Imhoff, 2004), neuroscience (Aston and Kirch, 2012), ocean-engineering (Nam et al, 2015) and bioinformatics (Rigaill et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A novel change-point approach for the detection of gas emission sources using remotely contained concentration data

Eckley¹,

Kirch²,

Weber³

2020

Ann. Appl. Stat.

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Motivated by an example from remote sensing of gas emission sources, we derive two novel change point procedures for multivariate time series where, in contrast to classical change point literature, the changes are not required to be aligned in the different components of the time series. Instead the change points are described by a functional relationship where the precise shape depends on unknown parameters of interest such as the source of the gas emission in the above example. Two different types of tests and the corresponding estimators for the unknown parameters describing the change locations are proposed. We derive the null asymptotics for both tests under weak assumptions on the error time series and show asymptotic consistency under alternatives. Furthermore, we prove consistency for the corresponding estimators of the parameters of interest. The small sample behavior of the methodology is assessed by means of a simulation study and the above remote sensing example analyzed in detail.

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“…Such an approach is used for classification by [1] and [5], the latter being restricted to count data. A HMM framework is also used by [26] in the related field of changepoint detection. Fitting a HMM has the drawback of being computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Dynamic classification using multivariate locally stationary wavelet processes

2018

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Methods for the supervised classification of signals generally aim to assign a signal to one class for its entire time span. In this paper we present an alternative formulation for multivariate signals where the class membership is permitted to change over time. Our aim therefore changes from classifying the signal as a whole to classifying the signal at each time point to one of a fixed number of known classes. We assume that each class is characterised by a different stationary generating process, the signal as a whole will however be nonstationary due to class switching. To capture this nonstationarity we use the recently proposed Multivariate Locally Stationary Wavelet model. To account for uncertainty in class membership at each time point our goal is not to assign a definite class membership but rather to calculate the probability of a signal belonging to a particular class. Under this framework we prove some asymptotic consistency results. This method is also shown to perform well when applied to both simulated and accelerometer data. In both cases our method is able to place a high probability on the correct class for the majority of time points.

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The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints

Cited by 10 publications

References 32 publications

BayesProject: Fast computation of a projection direction for multivariate changepoint detection

BayesProject: Fast computation of a projection direction for multivariate changepoint detection

A novel change-point approach for the detection of gas emission sources using remotely contained concentration data

Dynamic classification using multivariate locally stationary wavelet processes

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