A support vector machine based technique for online detection of outliers in transient time series

Authors

Hugo Martins
Luís Palma
Alberto Cardoso
Paulo José Carrilho de Sousa Gil

Abstract

This paper deals with online detection and accommodation
of outliers in transient time series by appealing
to a machine learning technique. The methodology is based on
a Least Squares Support Vector Machine technique together
with a sliding window-based learning algorithm. A modification
to this method is proposed so as to extend its application to
transient raw data collected from transmitters attached to a
Wireless Sensor Network. The performance of two approaches
are compared on a particular controlled data set.

Conference

10th Asian Control Conference, May 2015

DOI

Cited by

Year 2017 : 3 citations

Ojuroye, Olivia, et al. "Smart Textiles for Smart Home Control and Enriching Future Wireless Sensor Network Data." Sensors for Everyday Life. Springer International Publishing, 2017. 159-183.

Xie, Sai, and Zhe Chen. "Anomaly Detection and Redundancy Elimination of Big Sensor Data in Internet of Things." arXiv preprint arXiv:1703.03225 (2017).

"Smart Textiles for Smart Home Control and Enriching Future Wireless Sensor Network Data", Olivia Ojuroye, Russel Torah, Stephen P Beeby and Adriana Wilde, In book: Sensors for Everyday Life, pp.159-183, DOI: 10.1007/978-3-319-47319-2_9

Year 2016 : 1 citations

Liu, Ben. "Optimization of hierarchical data fusion in Wireless Sensor Networks." Electronics Information and Emergency Communication (ICEIEC), 2016 6th International Conference on. IEEE, 2016.