Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 @m by the algorithmic complexity of @m. Here  assume that  are at a time t>1 and have already observed x=x"1...x"t.  bound the future prediction performance on x"t"+"1x"t"+"2... by a new variant of algorithmic complexity of @m given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged.  also briefly discuss potential generalizations to Bayesian model classes and to classification problems.
['Alexey V. Chernov', 'Marcus Hutter', 'Jürgen Schmidhuber']
I am writing a research paper on a idea Algorithmic complexity bounds on future prediction errors
[{"from": "human", "value": "I am writing a research paper on a idea Algorithmic complexity bounds on future prediction errors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 @m by the algorithmic complexity of @m. Here  assume that  are at a time t>1 and have already observed x=x\"1...x\"t.  bound the future prediction performance on x\"t\"+\"1x\"t\"+\"2... by a new variant of algorithmic complexity of @m given x, plus the complexity of the randomness deficiency of x. The new complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged.  also briefly discuss potential generalizations to Bayesian model classes and to classification problems."}]