  We describe and experimentally investigate a method to construct forecasting
algorithms for stationary and ergodic processes based on universal measures (or
so-called universal data compressors). Using some geophysical and economical
time series as examples, we show that the precision of thus obtained
predictions is higher than that of known methods.
