Event detection in marine time series data

TY - GEN

T1 - Event detection in marine time series data

AU - Oehmcke, Stefan

AU - Zielinski, Oliver

AU - Kramer, Oliver

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Automatic detection of special events in large data is often more interesting for data analysis than regular patterns. In particular, the processes in multivariate time series data can be better understood, if a deviation from the normal behavior is found. In this work, we apply a machine learning event detection method to a new application in the marine domain. The marine long-term data from the stationary plat- form at Spiekeroog, called Time Series Station, are a challenge, because noise, sensor drifts and missing data complicate analysis of the data. We acquire labels for evaluation with help of experts and test different approaches, which include time context into patterns. The used event detection method is local outlier factor (LOF). To improve results, we apply dimensionality reduction to the data. The analysis of the results shows, that the machine learning techniques can find special events, which are of interest to experts in the field.

AB - Automatic detection of special events in large data is often more interesting for data analysis than regular patterns. In particular, the processes in multivariate time series data can be better understood, if a deviation from the normal behavior is found. In this work, we apply a machine learning event detection method to a new application in the marine domain. The marine long-term data from the stationary plat- form at Spiekeroog, called Time Series Station, are a challenge, because noise, sensor drifts and missing data complicate analysis of the data. We acquire labels for evaluation with help of experts and test different approaches, which include time context into patterns. The used event detection method is local outlier factor (LOF). To improve results, we apply dimensionality reduction to the data. The analysis of the results shows, that the machine learning techniques can find special events, which are of interest to experts in the field.

KW - Anomaly detection

KW - Event detection

KW - LOF

KW - Marine systems

KW - Time series

KW - Wadden sea

UR - http://www.scopus.com/inward/record.url?scp=84951875266&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-24489-1_24

DO - 10.1007/978-3-319-24489-1_24

M3 - Article in proceedings

AN - SCOPUS:84951875266

SN - 9783319244884

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 279

EP - 286

BT - KI 2015

A2 - Hölldobler, Steffen

A2 - Krötzsch, Markus

A2 - Rudolph, Sebastian

A2 - Peñaloza, Rafael

PB - Springer Verlag,

T2 - 38th Annual German Conference on Advances in Artificial Intelligence, AI 2015

Y2 - 21 September 2015 through 25 September 2015

ER -