美国新英格兰地区时空CP分解分析 (A Spatio-temporal CP decomposition analysis of New England region in the US)

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，从而导致了多维数据的兴起。本文不仅利用了数据的多维结构，还利用了其时间和空间结构。实际上，我们将使用NCAR气候数据网关网站，该网站为全球和区域气候模型数据提供数据发现和访问服务。将日总降水量（prec）、最高温度（tmax）和最低温度（tmin）的日值组合起来，创建了一个称为张量（多维数组）的多维数据。在本文中，我们提出了一种时空主成分分析方法来初始化CP分解分量。我们在CP分量分析的初始化步骤中充分利用了数据的空间和时间结构。通过与最流行的初始化方法进行比较，测试了我们方法的性能。我们还进行了聚类分析，以进一步展示我们分析的效果。