When using ordinal patterns, which describe the ordinal structure within a data vector, the problem of ties appeared permanently. So far, model classes were used which do not allow for ties; randomization has been another attempt to overcome this problem. Often, time periods with constant values even have been counted as times of monotone increase. To overcome this, a new approach is proposed: it explicitly allows for ties and, hence, considers more patterns than before. Ties are no longer seen as nuisance, but to carry valuable information. Limit theorems in the new framework are provided, both, for a single time series and for the dependence between two time series. The methods are used on hydrological data sets. It is common to distinguish five flood classes (plus 'absence of flood'). Considering data vectors of these classes at a certain gauge in a river basin, one will usually encounter several ties. Co-monotonic behavior between the data sets of two gauges (increasing, constant, decreasing) can be detected by the method as well as spatial patterns. Thus, it helps to analyze the strength of dependence between different gauges in an intuitive way. This knowledge can be used to asses risk and to plan future construction projects.

翻译：在使用描述数据向量内有序结构的有序模式时，平局问题始终存在。以往采用不允许平局的模型类别；随机化是另一种解决该问题的尝试。常数值时段甚至常被视作单调递增时段计算。为克服此问题，本文提出新方法：明确允许平局存在，因此考虑的模式数量多于以往。平局不再被视为障碍，而是承载有价值的信息。新框架下提供了针对单时间序列及两时间序列间依赖性的极限定理。该方法被应用于水文数据集。通常区分五种洪水等级（外加"无洪水"）。考虑流域某测站这些等级的数据向量时，通常会遇到多个平局。该方法可检测两测站数据集间的共单调行为（递增、恒定、递减）及空间模式。因此，它能以直观方式分析不同测站间的依赖强度。该知识可用于风险评估及未来工程规划。