Event Detection in Marine Time Series Data
Abstract: 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 platform 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.
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