Forecasts of TBM operational data based on time delayed prediction is pointless.

One step ahead predictions cannot contain valuable information for TBM tunneling.

Heterogeneous TBM data point spacing poses a challenge for machine learning.

Special accuracy measures and close up plots are needed for forecast evaluation.

In tunneling, predictions of the rockmass conditions ahead of the face are of great interest to be able to take appropriate countermeasures at the right time. Besides investigations like drilling or geophysics, new approaches in mechanized tunneling aim at forecasting the geology ahead via Machine Learning models. These models are trained to forecast tunnel boring machine data by learning from recorded data in already excavated parts of the tunnel. Simply judging from high accuracies, these results may look promising at the first sight, but forecasts like this are mostly just delayed and slightly altered versions of the input data and no predictive value can result from them. This paper shows deficits in the current practice of data driven forecasts ahead of the tunnel face and raises impetus for further research in this particular field and TBM data analysis in general.