In this paper we implemented the detrended multiple cross-correlation coefficient with sliding windows approach in order to measure multiple cross-correlation between time series as a function of time scale $n$, and a sliding time $\tau$. Faced with several non-stationary time series, a fixed size window will be defined by $w$, and the coefficients $DM{C}_{\mathrm{x}}^2$ are calculated for different time scales ($4\le n\le \frac{w}{4}$) and time $\tau$. Following this methodology, it was possible to set up a $DM{C}_{\mathrm{x}}^2$ cross-correlation color map for a simulated (random) and an empirical case. This new color map with sliding windows shows the time scale and the current day for a cross-correlation analysis. In this way, it is possible to analyze the multiple cross-correlation between three or more variables in different areas of knowledge, such as economics and finance.

在本文中，我们使用滑动窗口方法实现了去趋势的多重互相关系数，以便测量时间序列之间的多重互相关作为时间标度的函数 $ñ$，以及滑动时间 $\tau$。面对几个非平稳时间序列，固定大小的窗口将由$w$，以及系数 $d\mathrm{中号}{C}_{\mathrm{X}}^{\mathrm{2个}}$ 是针对不同的时间范围计算的（$4\le ñ\le \frac{w}{4}$）和时间 $\tau$。按照这种方法，可以建立一个$d\mathrm{中号}{C}_{\mathrm{X}}^{\mathrm{2个}}$模拟（随机）和经验案例的互相关色图。带有滑动窗口的新色图显示了时间标度和当前日期，以进行互相关分析。这样，可以分析不同知识领域（例如，经济和金融）中三个或更多变量之间的多重互相关。