  Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for
space weather disturbances. In the past, different approaches have been used to
automatically detect events in existing time series resulting from solar wind
in situ observations. However, accurate and fast detection still remains a
challenge when facing the large amount of data from different instruments. For
the automatic detection of ICMEs we propose a pipeline using a method that has
recently proven successful in medical image segmentation. Comparing it to an
existing method, we find that while achieving similar results, our model
outperforms the baseline regarding training time by a factor of approximately
20, thus making it more applicable for other datasets. The method has been
tested on in situ data from the Wind spacecraft between 1997 and 2015 with a
True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected
correctly by our algorithm, producing a total of 254 False Positives.
Additionally, it produced reasonable results on datasets with fewer features
and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill
Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find
the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56
minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively
fast training allows straightforward tuning of hyperparameters and could
therefore easily be used to detect other structures and phenomena in solar wind
data, such as corotating interaction regions.
