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Exploring the use of machine learning for anomaly detection and editing
We are assessing a selection of machine learning and traditional approaches, ranging from simple techniques to more-complex methods suited to the time-series nature of some datasets. The aim is to develop a toolbox of techniques suited to datasets with different characteristics.
Initial work has focussed on data where there is limited understanding of the behaviour patterns of correct vs wrong data, where a key challenge is to develop a labelled dataset that the methods can be assessed against. Unsupervised approaches such as Local Outlier Factor can assist to identify anomalies, however human expertise is needed to help differentiate wrong data from unusual data.
The ABS is engaging with other National Statistical Organisations who are undertaking similar work in order to share best practice.
For further information, please contact Jenny Pocknee at firstname.lastname@example.org.
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1504.0 - Methodological News, Mar 2021
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 29/03/2021