Sequences formed by symbols are found in diverse fields, including genome sequences, written texts and computer codes. An interesting question is whether a sequence of symbols contains correlated structures. Existing methods to characterize correlations require a numerical representation of the sequence. In this regard, mapping a sequence of text into a sequence of numerical values is a key step for assessing correlation analysis. This work proposes a methodology to study correlations in a sequence of symbols. In the first step, the sequence of symbols is mapped in a multivariate numerical sequence formed by unit vectors in a vectorial space. The main feature of such mapping is that symbols are equally weighted, thus avoiding the numerical overrepresentation of symbols. In the second step, a multivariate version of the detrended fluctuation analysis is used to quantify correlations in the numerical sequence. Genome sequences (first COVID-19), written English texts and comovements between Bitcoin and gold markets were used to illustrate the proposed methodology’s performance. The results showed that the balanced numerical mapping of symbolic sequences and the multivariate DFA provides valuable insights into the correlations in a sequence of symbols.

由符号形成的序列存在于不同领域，包括基因组序列、书面文本和计算机代码。一个有趣的问题是符号序列是否包含相关结构。现有的表征相关性的方法需要序列的数字表示。在这方面，将文本序列映射为数值序列是评估相关性分析的关键步骤。这项工作提出了一种研究符号序列中相关性的方法。在第一步中，符号序列被映射到由向量空间中的单位向量形成的多元数值序列中。这种映射的主要特点是符号的权重相等，从而避免了符号的数值过度表示。在第二步中，使用去趋势波动分析的多变量版本来量化数值序列中的相关性。基因组序列（第一个 COVID-19）、书面英文文本以及比特币和黄金市场之间的联动被用来说明所提出的方法的性能。结果表明，符号序列的平衡数值映射和多元 DFA 为符号序列中的相关性提供了有价值的见解。