Anomaly detection in streaming environmental sensor data: A data-driven modeling approach

By David J. Hill, Barbara S. Minsker. Environmental Modelling & Software, 2010

298 citations recorded by the source corpus.

How researchers see this paper

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Tackling Climate Change with Machine Learning (2019, background): “In sensor networks, individual nodes fail frequently, but are redundant by design – this is an opportunity for research into anomaly detection and missing data imputation [493, 494].”
A Review on Outlier/Anomaly Detection in Time Series Data (2020, methodology): “Other methods use an autoregressive model [Hill and Minsker 2010] or an ARIMA model [Zhang et al. 2012], which obtain confidence intervals for the predictions instead of only point estimates.”
Outlier Detection for Temporal Data: A Survey (2014, methodology): “Other prediction models include Multilayer perceptron (MLP) predictor [75] and support vector regression [76].”