Experimental Investigation of Forecasting Methods Based on Universal   Measures
  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. 
Boris Ryabko and Pavel Pristavka
cs.IT math.IT physics.data-an