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Erroneous GNSS positioning, failures in spacecraft operations and poweroutages due to geomagnetically induced currents are severe threats originatingfrom space weather. Having knowledge of potential impacts on modern society inadvance is key for many end-user applications. This covers not only the timingof severe geomagnetic storms but also predictions of substorm onsets at polarlatitudes. In this study we aim at contributing to the timing problem of spaceweather impacts and propose a new method to predict the solar wind propagationdelay between Lagrangian point L1 and the Earth based on machine learning,specifically decision tree models. The propagation delay is measured from theidentification of interplanetary discontinuities detected by the AdvancedComposition Explorer (ACE) and their subsequent sudden commencements in themagnetosphere recorded by ground-based magnetometers. A database of thepropagation delay has been constructed on this principle including 380interplanetary shocks. The feature set consists of six features, the threecomponents of each the solar wind speed and position of ACE around L1. Themachine learning results are compared to the flat propagation delay and themethod 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 solarwind propagation delay and also outperforms the physical flat and vector delaymodels by 50 \% and 15 \% respectively. To increase the confidence in thepredictions of the trained GB model, we perform a performance validation,provide feature importance and analyse the feature impact on the model outputwith Shapley values. The major advantage of the machine learning approach isits simplicity when it comes to its application. After training, input fromonly one ACE datapoint have to be fed into the algorithm for a prediction.

Timing of the solar wind propagation delay between L1 and Earth based on machine learning