  The prediction of complex nonlinear dynamical systems with the help of
machine learning techniques has become increasingly popular. In particular,
reservoir computing turned out to be a very promising approach especially for
the reproduction of the long-term properties of a nonlinear system. Yet, a
thorough statistical analysis of the forecast results is missing. Using the
Lorenz and R\"ossler system we statistically analyze the quality of prediction
for different parametrizations - both the exact short-term prediction as well
as the reproduction of the long-term properties (the "climate") of the system
as estimated by the correlation dimension and largest Lyapunov exponent. We
find that both short and longterm predictions vary significantly among the
realizations. Thus special care must be taken in selecting the good predictions
as predictions which deliver better short-term prediction also tend to better
resemble the long-term climate of the system. Instead of only using purely
random Erd\"os-Renyi networks we also investigate the benefit of alternative
network topologies such as small world or scale-free networks and show which
effect they have on the prediction quality. Our results suggest that the
overall performance with respect to the reproduction of the climate of both the
Lorenz and R\"ossler system is worst for scale-free networks. For the Lorenz
system there seems to be a slight benefit of using small world networks while
for the R\"ossler system small world and Erd\"os -Renyi networks performed
equivalently well. In general the observation is that reservoir computing works
for all network topologies investigated here.
