  Predicting the Bz magnetic field embedded within ICMEs, also known as the Bz
problem, is a key challenge in space weather forecasting. We study the
hypothesis that upstream in situ measurements of the sheath region and the
first few hours of the magnetic obstacle provide sufficient information for
predicting the downstream Bz component. To do so, we develop a predictive tool
based on machine learning that is trained and tested on 348 ICMEs from Wind,
STEREO-A, and STEREO-B measurements. We train the machine learning models to
predict the minimum value of the Bz component and the maximum value of the
total magnetic field Bz in the magnetic obstacle. To validate the tool, we let
the ICMEs sweep over the spacecraft and assess how continually feeding in situ
measurements into the tool improves the Bz prediction. Because the application
of the tool in operations needs an automated detection of ICMEs, we implement
an existing automated ICME detection algorithm and test its robustness for the
time intervals under scrutiny. We find that the predictive tool can predict the
minimum value of the Bz component in the magnetic obstacle with a mean absolute
error of 3.12 nT and a Pearson correlation coefficient of 0.71 when the sheath
region and the first 4 hours of the magnetic obstacle are observed. While the
underlying hypothesis is unlikely to solve the Bz problem, the tool shows
promise for ICMEs that have a recognizable magnetic flux rope signature.
Transitioning the tool to operations could lead to improved space weather
forecasting.
