Detecting anomalies in water-quality variables utilising the temporal correlation

Опубликовано: 08 Июнь 2026
на канале: ACEMS - ARC Centre of Excellence for Mathematical & Statistical Frontiers

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ARC Linkage Project Workshop: Revolutionising water quality monitoring in the information age.
Dr Puwasala Gamakumara, Monash University
Puwasala Gamakumara provides an overview of a novel anomaly detection approach implemented in the R package conduits. The generalised additive model uses conditional cross-correlation between data from pairs of sensors to estimate lag times and can accommodate covariates, which improves the performance of the algorithm.

Github repository containing R scripts and presentation used in the conduits package demonstration
https://github.com/PuwasalaG/ARCLP-wo...

Github repository for conduits package for R Statistical Software
https://github.com/PuwasalaG/conduits