Time series segmentation of linear stochastic processes for anomaly detection problem using supervised methods
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
56th ESReDA (European Safety, Reliability & Data Association) Seminar
Sprache des Tagungstitel:
Englisch
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.
Sprache der Kurzfassung:
Englisch
Vortragstyp:
Hauptvortrag / Eingeladener Vortrag auf einer Tagung
Forschungs-