Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well.
We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

时变数据分析是诸多应用领域中的重要研究问题，而时序数据的交互式可视化则有助于理解大规模时序数据中的潜在模式。目前已有诸多高效方法可用于单变量时序数据的可视化分析，支持数据质量评估、异常值检测、周期性或高频出现模式识别等任务。但支持多变量时序数据的可视化分析方法则相对匮乏。由于每个时间戳对应多个数值，多变量时序分析任务本身难度更高，且现有可视化技术往往扩展性不佳。
本文提出一种面向大规模多变量时变数据的可视化分析方法，其核心思路是将多变量测量数据投影至二维显示空间，并通过轨迹可视化时间维度。我们采用基于相似数据元素分组的可视化数据聚合隐喻，以适配多变量时序数据的尺度需求。聚合流程既可基于数据的统计特性实现，也可依托数据聚类算法完成。通过合理定义的用户控件，可实现数据的浏览与探索，并交互式调整数据聚合的参数以优化数据分析流程。本文给出了该方法的具体实现，并将其应用于地球观测领域的大规模数据集，验证了所提方法的适用性与实用价值。