Event detection in marine time series data
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Event detection in marine time series data. / Oehmcke, Stefan; Zielinski, Oliver; Kramer, Oliver.
KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Proceedings. ed. / Steffen Hölldobler; Markus Krötzsch; Sebastian Rudolph; Rafael Peñaloza. Springer Verlag, 2015. p. 279-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9324).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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 -
ID: 223196594