@inproceedings{104ca34b831946f1882bf79187a5d227,
title = "Outliers detection in non-stationary time-series: Support vector machine versus principal component analysis",
abstract = "This paper aims at comparing two local outliers detection techniques. One is based on a Least Squares Support Vector Machine technique within a sliding window-based learning algorithm. A modification is proposed to improve its performance in non-stationary time-series. The second method relies on the Principal Component Analysis theory along with a robust orthonormal projection approximation subspace tracking with rank-1 modification. The comparative performance of these methods are assessed through simulations using a non stationary time-series generated with a nonlinear input-output model.",
keywords = "WIRELESS SENSOR NETWORKS, ANOMALY DETECTION, ALGORITHM",
author = "P. Gil and H. Martins and A. Cardoso and L. Palma",
note = "sem pdf conforme despacho. partially supported by Project CENTRO-07-ST24-FEDER-002003 (iCIS-Intelligent Computing in the Internet of Services.",
year = "2016",
month = jun,
day = "1",
doi = "10.1109/ICCA.2016.7505361",
language = "English",
series = "IEEE International Conference on Control and Automation ICCA",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "701--706",
booktitle = "2016 12th IEEE International Conference on Control and Automation (ICCA)",
address = "United States",
}