  Erroneous GNSS positioning, failures in spacecraft operations and power
outages due to geomagnetically induced currents are severe threats originating
from space weather. Having knowledge of potential impacts on modern society in
advance is key for many end-user applications. This covers not only the timing
of severe geomagnetic storms but also predictions of substorm onsets at polar
latitudes. In this study we aim at contributing to the timing problem of space
weather impacts and propose a new method to predict the solar wind propagation
delay between Lagrangian point L1 and the Earth based on machine learning,
specifically decision tree models. The propagation delay is measured from the
identification of interplanetary discontinuities detected by the Advanced
Composition Explorer (ACE) and their subsequent sudden commencements in the
magnetosphere recorded by ground-based magnetometers. A database of the
propagation delay has been constructed on this principle including 380
interplanetary shocks. The feature set consists of six features, the three
components of each the solar wind speed and position of ACE around L1. The
machine learning results are compared to the flat propagation delay and the
method based on the normal vector of solar wind discontinuities (vector delay).
The ML model achieves an RMSE of 4.5 min with respect to the measured solar
wind propagation delay and also outperforms the physical flat and vector delay
models by 50 \% and 15 \% respectively. To increase the confidence in the
predictions of the trained GB model, we perform a performance validation,
provide feature importance and analyse the feature impact on the model output
with Shapley values. The major advantage of the machine learning approach is
its simplicity when it comes to its application. After training, input from
only one ACE datapoint have to be fed into the algorithm for a prediction.
