Joint assessment of data predictability and quality predictors

The paper proposes and analyzes a new approach to the selection of predictors necessary for predicting future values in data sequences in a specific time period. Our goal is low-cost implemented techniques that ensure the selection of an acceptable predictor for the current prediction session, or the decision about the impossibility of making a reliable forecast if one finds that this section of the sequence does not have the predictability property. For this, the predictability of this sequence is defined as the maximum conditional probability of the correct prediction in the set of available predictors for a given set of observed values. The selection of predictors is performed by both the magnitude of the conditional probability estimation and the degree of difference between a specific predictor and a predictor that is optimal for predicting the next outcome of the Bernoulli trials sequence.