Spatio temporal data consist of measurement for one or more raster fields such as weather, traffic volume, crime rate, or disease incidents. Advances in modern technology have increased the number of available information for this type of data hence the rise of multidimensional data. In this paper we take advantage of the multidimensional structure of the data but also its temporal and spatial structure. In fact, we will be using the NCAR Climate Data Gateway website which provides data discovery and access services for global and regional climate model data. The daily values of total precipitation (prec), maximum (tmax), and minimum (tmin) temperature are combined to create a multidimensional data called tensor (a multidimensional array). In this paper, we propose a spatio temporal principal component analysis to initialize CP decomposition component. We take full advantage of the spatial and temporal structure of the data in the initialization step for cp component analysis. The performance of our method is tested via comparison with most popular initialization method. We also run a clustering analysis to further show the performance of our analysis.

翻译：时空数据包含一个或多个栅格场的测量值，例如天气、交通流量、犯罪率或疾病事件。现代技术的进步增加了此类数据的可用信息量，从而推动了多维数据的兴起。本文充分利用数据的多维结构及其时空结构。具体而言，我们将利用NCAR气候数据网关网站，该网站提供全球和区域气候模型数据的数据发现与访问服务。通过整合日总降水量（prec）、最高温度（tmax）和最低温度（tmin）的数值，构建称为张量（多维数组）的多维数据集。本文提出一种时空主成分分析方法，用于初始化CP分解分量。在CP分量分析的初始化步骤中，我们充分利用数据的空间与时间结构。通过与最常用的初始化方法进行比较，验证了所提方法的性能。此外，我们还进行了聚类分析，以进一步展示本分析方法的有效性。