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Perfect short-term prediction of binary patterns was revealed in binary data sets of highly complex nature. These binary patterns, referred to as perfect predictors, yield risk-free prediction of the value of the next bit of a binary sequence. The method was tested on binary data sets generated by applying a simple binary transformation on the data of the… (More)

Complex binary sequences were generated by applying a simple threshold, linear transformation to the logistic iterative function, xn+1 = r xn (1 − xn). Depending primarily on the value of the non-linearity parameter r, the logistic function exhibits a great variety of behavior, including stable states, cycling and periodical activity and the period doubling… (More)

Complex binary sequences are generated through the application of simple threshold, linear transformations to the logistic iterative map. Depending primarily on the value of its non-linearity parameter, the logistic map exhibits a great variety of behavior, including stable states, cycling and periodical activity and the period doubling phenomenon that… (More)

We investigate the existence of rules (in the form of binary patterns) that allow the short-term prediction of highly complex binary sequences. We also study the extent to which these rules retain their predictive power when the sequence is contaminated with noise. Complex binary sequences are derived by applying two binary transformations on real-valued… (More)

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