  Various neural network based methods are capable of anticipating human body
motions from data for a short period of time. What these methods lack are the
interpretability and explainability of the network and its results. We propose
to use Dynamic Mode Decomposition with delays to represent and anticipate human
body motions. Exploring the influence of the number of delays on the
reconstruction and prediction of various motion classes, we show that the
anticipation errors in our results are comparable or even better for very short
anticipation times ($<0.4$ sec) to a recurrent neural network based method. We
perceive our method as a first step towards the interpretability of the results
by representing human body motions as linear combinations of ``factors''. In
addition, compared to the neural network based methods large training times are
not needed. Actually, our methods do not even regress to any other motions than
the one to be anticipated and hence is of a generic nature.
