Scholarlink

<p>SpatIotemporal Gaussian Mixture correlAtion transformer (SIGMAformer) is a spatiotemporal forecasting architecture that integrates a Gaussian mixture pattern extractor (GMPE) with a dynamic spatiotemporal correlation (DSTC) mechanism. The DSTC module leverages GMPE to automatically compute spatiotemporal pattern-specific weights from the data. These weights are first used to calculate temporal correlations within each station and then integrated with global pattern weights to evaluate spatial correlations across stations. This nonlinear, dynamically adaptive modeling approach emphasizes critical spatiotemporal patterns while suppressing less relevant ones. Experiments on global weather datasets reveal that SIGMAformer consistently outperforms state-of-the-art forecasting models and significantly improves wind speed prediction. Removing DSTC increased the mean squared error values by up to 7.18% and 7.22% for wind speed and temperature predictions, respectively. These findings underscore SIGMAformer’s capacity to capture essential spatiotemporal patterns and establish a scalable methodology for intelligent sensor-network fusion in environmental forecasting.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月23日

查看全文
  • Do-Yeon Kim,
  • Heung-Il Suk

摘要

SpatIotemporal Gaussian Mixture correlAtion transformer (SIGMAformer) is a spatiotemporal forecasting architecture that integrates a Gaussian mixture pattern extractor (GMPE) with a dynamic spatiotemporal correlation (DSTC) mechanism. The DSTC module leverages GMPE to automatically compute spatiotemporal pattern-specific weights from the data. These weights are first used to calculate temporal correlations within each station and then integrated with global pattern weights to evaluate spatial correlations across stations. This nonlinear, dynamically adaptive modeling approach emphasizes critical spatiotemporal patterns while suppressing less relevant ones. Experiments on global weather datasets reveal that SIGMAformer consistently outperforms state-of-the-art forecasting models and significantly improves wind speed prediction. Removing DSTC increased the mean squared error values by up to 7.18% and 7.22% for wind speed and temperature predictions, respectively. These findings underscore SIGMAformer’s capacity to capture essential spatiotemporal patterns and establish a scalable methodology for intelligent sensor-network fusion in environmental forecasting.