Many geological phenomena are regularly measured over time to follow developments and changes. For many of these phenomena, the absolute values are not of interest, but rather the relative information, which means that the data are compositional time series. Thus, the serial nature and the compositional geometry should be considered when analyzing the data. Multivariate time series are already challenging, especially if they are higher dimensional, and latent variable models are a popular way to deal with this kind of data. Blind source separation techniques are well-established latent factor models for time series, with many variants covering quite different time series models. Here, several such methods and their assumptions are reviewed, and it is shown how they can be applied to high-dimensional compositional time series. Also, a novel blind source separation method is suggested which is quite flexible regarding the assumptions of the latent time series. The methodology is illustrated using simulations and in an application to light absorbance data from water samples taken from a small stream in Lower Austria.

随着时间的推移，许多地质现象都会被定期测量，以跟踪其发展和变化。对于许多这样的现象，绝对值并不重要，而是相对信息，这意味着数据是组合时间序列。因此，在分析数据时应考虑序列性质和组合几何形状。多元时间序列已经具有挑战性，特别是当它们是高维的时，而潜变量模型是处理此类数据的流行方法。盲源分离技术是成熟的时间序列潜在因子模型，有许多变体涵盖了完全不同的时间序列模型。在这里，我们回顾了几种这样的方法及其假设，并展示了如何将它们应用于高维组合时间序列。此外，还提出了一种新颖的盲源分离方法，该方法对于潜在时间序列的假设非常灵活。该方法通过模拟以及对采自下奥地利小溪的水样的光吸收数据的应用来说明。