  Geomagnetic storms resulting from high-speed streams can have significant
negative impacts on modern infrastructure due to complex interactions between
the solar wind and geomagnetic field. One measure of the extent of this effect
is the Kyoto $Dst$ index. We present a method to predict $Dst$ from data
measured at the Lagrange 5 (L5) point, which allows for forecasts of solar wind
development 4.5 days in advance of the stream reaching the Earth. Using the
STEREO-B satellite as a proxy, we map data measured near L5 to the near-Earth
environment and make a prediction of the $Dst$ from this point using the
Temerin-Li $Dst$ model enhanced from the original using a machine learning
approach. We evaluate the method accuracy with both traditional point-to-point
error measures and an event-based validation approach. The results show that
predictions using L5 data outperform a 27-day solar wind persistence model in
all validation measures but do not achieve a level similar to an L1 monitor.
Offsets in timing and the rapidly-changing development of $B_z$ in comparison
to $B_x$ and $B_y$ reduce the accuracy. Predictions of $Dst$ from L5 have an
RMSE of $9$ nT, which is double the error of $4$ nT using measurements
conducted near the Earth. The most useful application of L5 measurements is
shown to be in predicting the minimum $Dst$ for the next four days. This method
is being implemented in a real-time forecast setting using STEREO-A as an L5
proxy, and has implications for the usefulness of future L5 missions.
