Chernov, AlexeyHutter, Marcus2015-12-10October 8-354029242Xhttp://hdl.handle.net/1885/57726We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution μ by the algorithmic complexity of μ. Here we assume that we arCopyright Information: © Springer-Verlag Berlin Heidelberg 2005. http://www.sherpa.ac.uk/romeo/issn/0302-9743/..."Author's post-print on any open access repository after 12 months after publication" from SHERPA/RoMEO site (as at 31/08/15)Copyright Information: © 2006 Elsevier Inc. http://www.sherpa.ac.uk/romeo/issn/0890-5401/..."Author's post-print on open access repository after an embargo period of between 12 months and 48 months" from SHERPA/RoMEO site (as at 28/08/15)Keywords: Kolmogorov complexity; Monotone conditional complexity; Online sequential prediction; Posterior bounds; Randomness deficiency; Solomonoff prior; Algorithms; Bayesian networks; Computational complexity; Forecasting; Random processes; Parallel processing sy Future loss; Kolmogorov complexity; Monotone conditional complexity; Online sequential prediction; Posterior bounds; Randomness deficiency; Solomonoff prior; Total error200510.1016/j.ic.2006.10.0042016-02-24