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arXiv Machine Learning By Rajko Turudija, Du\v{s}an Stojiljkovi\'c, Milan Zdravkovi\'c, Marko Ignjatovi\'c

Towards an approach to multivariate outlier detection for District Heating System data

Read the original on arXiv Machine Learning →

arXiv:2608. 11375v1 Announce Type: new Abstract: In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling's T-squared test.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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