Experimental Investigation of Forecasting Methods Based on Universal Measures

Abstract

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.

submitted

References in corpus (1)

• Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series