Dmitry Efrosinin, Valentin Sturm,
"Time series segmentation of linear stochastic processes for anomaly detection problem using supervised methods"
, in Zutautaite, I., Eid, M., Simola, K. and Kopustinskas, V.: Critical Services continuity, Resilience and Security: Proceedings of the 56th ESReDA Seminar, Publications Office of the European Union, JRC, Luxembourg, Seite(n) 81-91, 2019, ISBN: 978-92-76-13359-9
Original Titel:
Time series segmentation of linear stochastic processes for anomaly detection problem using supervised methods
Sprache des Titels:
Englisch
Original Buchtitel:
Critical Services continuity, Resilience and Security: Proceedings of the 56th ESReDA Seminar
Original Kurzfassung:
The problem of time series segmentation for real-world applications has received
much attention recently. Different industrial machines as elements of critical
infrastructure for energy extraction, pumping, generation and other operations are
equipped by hundreds of sensors which measure and evaluate variety data sets such
as temperature, vibration, accelerations, pressure, voltage and so on. In many cases
these measurements are unreliable, incomplete, inconsistent, and noisy and hence
they can be interpreted as realizations of some linear stochastic processes. The task
of recognizing of anomalous operation mode of machines can be reduced to the
problem of pattern recognition, change point detection or segmentation in time
series. In this paper we propose a general approach for time series segmentation of
linear stochastic processes based on supervised learning algorithms which are
machine learning algorithms using a mapping from input samples to a target
attribute of the data. We also perform empirical examples for some hypothetical time
series segmentation.
Forschungs-