A Spatio-temporal CP decomposition analysis of New England region in the US
This work addresses the challenge of efficiently processing multidimensional climate data for researchers in environmental science, but it is incremental as it focuses on initialization improvements rather than a new paradigm.
The paper tackled the problem of analyzing spatio-temporal climate data by proposing a CP decomposition initialization method that leverages spatial and temporal structures, resulting in improved performance compared to popular initialization methods and validated through clustering analysis.
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.