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Model. Comput. Simul."],"published-print":{"date-parts":[[2012,3]]},"abstract":"<jats:p>Due to the relevance of self-similarity analysis in several research areas, there is an increased interest in methods to generate realizations of self-similar processes, namely in the ones capable of simulating long-range dependence. This article describes a new algorithm to approximate persistent fractional Brownian motions with a predefined Hurst parameter. The algorithm presents a computational complexity of<jats:italic>O<\/jats:italic>(<jats:italic>n<\/jats:italic>) and generates sequences with<jats:italic>n<\/jats:italic>(<jats:italic>n<\/jats:italic>\u2208 N) values with a small multiple of log<jats:sub>2<\/jats:sub>(<jats:italic>n<\/jats:italic>) variables. Because it operates in a sequential manner, the algorithm is suitable for simulations demanding real-time operation. 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