We introduce a general method for the study of memory in symbolic sequences based on higher-order Markov analysis. The Markov process that best represents a sequence is expressed as a mixture of matrices of minimal orders, enabling the definition of the so-called memory profile, which unambiguously reflects the true order of correlations. The method is validated by recovering the memory profiles of tunable synthetic sequences. Finally, we scan real data and showcase with practical examples how our protocol can be used to extract relevant stochastic properties of symbolic sequences.

翻译：我们采用了基于高阶Markov分析的象征性序列内存研究一般方法。最能代表一个序列的Markov过程表现为最低顺序矩阵的混合体,能够对所谓的记忆剖面进行定义,明确反映真实关联的顺序。该方法通过恢复可金枪鱼合成序列的内存剖面加以验证。最后,我们扫描真实数据,并以实例展示如何利用我们的协议提取符号序列的相关随机特性。