  Decades of studies have suggested several criteria to detect Interplanetary
coronal mass ejections (ICME) in time series from in-situ spacecraft
measurements. Among them the most common are an enhanced and smoothly rotating
magnetic field, a low proton temperature and a low plasma beta. However, these
features are not all observed for each ICME due to their strong variability.
Visual detection is time-consuming and biased by the observer interpretation
leading to non exhaustive, subjective and thus hardly reproducible catalogs.
Using convolutional neural networks on sliding windows and peak detection, we
provide a fast, automatic and multi-scale detection of ICMEs. The method has
been tested on the in-situ data from WIND between 1997 and 2015 and on the 657
ICMEs that were recorded during this period. The method offers an unambiguous
visual proxy of ICMEs that gives an interpretation of the data similar to what
an expert observer would give. We found at a maximum 197 of the 232 ICMEs of
the 2010-2015 period (recall 84 +-4.5 % including 90% of the ICMEs present in
the lists of Nieves-Chinchilla et al. (2015) and Chi et al. (2016). The minimal
number of False Positives was 25 out of 158 predicted ICMEs (precision
84+-2.6%). Although less accurate, the method also works with one or several
missing input parameters. The method has the advantage of improving its
performance by just increasing the amount of input data. The generality of the
method paves the way for automatic detection of many different event signatures
in spacecraft in-situ measurements.
