The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most important processes that fall under this domain, and forecasting it has become a crucial part of our daily routine. Weather data analysis is considered the most complex and challenging task. Although numerical weather prediction models are currently state-of-the-art, they are resource intensive and time-consuming. Numerous studies have proposed time-series-based models as a viable alternative to numerical forecasts. Recent research has primarily focused on forecasting weather at a specific location. Therefore, models can only capture temporal correlations. This self-contained paper explores various methods for regional data-driven weather forecasting, i.e., forecasting over multiple latitude-longitude points to capture spatiotemporal correlations. The results showed that spatiotemporal prediction models reduced computational cost while improving accuracy; in particular, the proposed tensor train dynamic mode decomposition-based forecasting model has comparable accuracy to ConvLSTM without the need for training. We use the NASA POWER meteorological dataset to evaluate the models and compare them with the current state of the art.
翻译:大多数实际世界过程都是时空的,它们产生的数据在空间和时间上都有进化。天气是属于这个领域的最重要的过程之一,预测它已成为我们日常工作的一个关键部分。天气数据分析被认为是最复杂和最具挑战性的任务。虽然数字天气预测模型目前是最先进的,但它们是资源密集和耗时的。许多研究都提出了基于时间序列的模型,作为数字预测的可行替代方法。最近的研究主要侧重于特定地点的天气预报。因此,模型只能捕捉时间相关关系。这篇自成一体的论文探索了区域数据驱动天气预报的各种方法,即多纬度点的预报,以捕捉多时空的相互关系。结果显示,空间时空预测模型在提高准确性的同时降低了计算成本;特别是,拟议的高压火车动态模式分位预测模型在不需要培训的情况下与CONLSTM具有可比性。我们使用美国航天局的POWER气象数据集来评估模型,并将模型与目前艺术状态进行比较。