Data collection within a real-world environment may be compromised by several factors such as data-logger malfunctions and communication errors, during which no data is collected. As a consequence, appropriate tools are required to handle the missing values when analysing and processing such data. This problem is often tackled via matrix decomposition. While it has been successfully applied in a wide range of applications, in this work we report an issue that has been neglected in literature and “degenerates” the quality of the imputations obtained by matrix decomposition in multivariate time-series (with smooth evolution). Briefly, the problem consists of the misalignment of the matrix decomposition result: the missing values imputations fall within an incorrect range of values and the transitions between observed and imputed values are not smooth. We address this problem by proposing a post-processing alignment strategy. According to our experiments, the post-processing adjustment substantially improves the accuracy of the imputations (when the misalignment occurs). Moreover, the results also suggest that the misalignment occurs mostly when dealing with a small number of time-series due to lack of generalization ability.

现实环境中的数据收集可能会受到多种因素的影响，例如数据记录器故障和通信错误，在此期间未收集任何数据。因此，在分析和处理此类数据时，需要适当的工具来处理缺失值。这个问题通常通过矩阵分解来解决。尽管它已被成功地广泛应用，但在这项工作中，我们报告了一个在文献中被忽略的问题，它“退化”了多元时间序列中矩阵分解获得的插补的质量（具有平稳的演化）。简而言之，问题包括矩阵分解结果的未对准：缺失值的估算落入了错误的值范围内，观察值与估算值之间的过渡不平滑。我们通过提出后处理对齐策略来解决此问题。根据我们的实验，后期处理调整可显着提高插补的准确性（发生未对准时）。此外，结果还表明，由于缺乏泛化能力，当处理少量时间序列时，不对齐主要发生。