An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Non-stochastic infinite and finite sequences
Theoretical Computer Science - Special issue Kolmogorov complexity
Calibration with many checking rules
Mathematics of Operations Research
Prediction, Learning, and Games
Prediction, Learning, and Games
On sequences with non-learnable subsequences
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
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In the last decade, new methods of forecasting were developed different from traditional statistical methods. In particular, it is possible to "efficiently" predict any sequence of outcomes without using any hypothesis on the nature of a source generating it. In the present paper, a modified version of the universal forecasting algorithm is considered. The main part of the paper is devoted to algorithmic analysis of universal forecasting methods and to exploring limits of their performance.