Change-point detection, segmentation, and related topics

Bardet, Jean-Marc; Brault, Vincent; Dachian, Sergueı̈; Enikeeva, Farida; Saussereau, Bruno

doi:10.1051/proc/202068006

Cited by 2 publications

(1 citation statement)

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“…The present manuscript relates to the offline framework, by supposing the full data has been recorded, and the segmentation is done posterior wise. For recent and detailed reviews of the offline changepoint detection, the reader may refer to Bardet, Jean-Marc, Brault, Vincent, Dachian, Serguei, Enikeeva, Farida, and Saussereau, Bruno (2020); Truong, Oudre, and Vayatis (2020). While the literature on change-point detection is abundant, applications to spatial data are somewhat limited.…”

Section: Introductionmentioning

confidence: 99%

Pesticide concentration monitoring: Investigating spatio‐temporal patterns in left censored data

2022

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Monitoring pesticide concentration is very important for public authorities given the major concerns for environmental safety and the likelihood for increased public health risks. An important aspect of this process consists in locating abnormal signals, from a large amount of collected data. This kind of data is usually complex since it suffers from limits of quantification leading to left censored observations, and from the sampling procedure which is irregular in time and space across measuring stations. The present manuscript tackles precisely the issue of detecting spatio-temporal collective anomalies in pesticide concentration levels, and introduces a novel methodology for dealing with spatio-temporal heterogeneity. The latter combines a change-point detection procedure applied to the series of maximum daily values across all stations, and a clustering step aimed at a spatial segmentation of the stations. Limits of quantification are handled in the change-point procedure, by supposing an underlying left-censored parametric model, piece-wise stationary. Spatial segmentation takes into account the geographical conditions, and may be based on river network, wind directions, etc. Conditionally to the temporal segment and the spatial cluster, one may eventually analyse the data and identify contextual anomalies. The proposed procedure is illustrated in detail on a data set containing the prosulfocarb concentration levels in surface waters in Centre-Val de Loire region.

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Section: Introductionmentioning

confidence: 99%