基于T-GCN-Luong注意力和GRU模型的可变重量组合的时间序列预测
Yushu Guo1,2, Jiacheng Huang3, Xuchu Jiang4
1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
Scientific reports
|July 2, 2025
概括
准确的温度预测对于减轻极端天气影响至关重要. 结合时间图卷积网络 (T-GCN),Luong注意力 (LUA) 和封闭循环单位 (GRU) 的新模型通过利用时空数据显著提高了预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 传统的温度预测方法与高不确定性和极端天气事件作斗争.
- 准确的时空温度预测对于社会适应和风险管理至关重要.
研究的目的:
- 开发一种新的可变重量组合模型,用于增强温度预测.
- 通过整合时空特征和注意力机制来提高预测准确度.
主要方法:
- 这是一个混合模型,结合了时间图卷积网络 (T-GCN),Luong Attention (LUA) 和Gated Recurrent Unit (GRU).
- T-GCN捕获时间空间的依赖性;LUA权衡输入时间步骤;GRU精确预测.
- 一种可变重量组合策略将T-GCN-LUA和GRU输出融合为最终预测.
主要成果:
- 整合空间信息显著提高了温度预测的有效性.
- 的注意力机制通过有效加权时间步骤,提高了T-GCN的准确性.
- 拟议的TGLAG模型在15个地点展示了卓越的性能,超过了GRU和T-GCN-LUA模型.
结论:
- 与GRU相比,TGLAG模型在温度预测准确度上提供了显著的改进,减少了RMSE约31.9%和MAE约26.9%,与GRU相比.
- 该模型显示出在温度预测和极端天气影响缓解方面具有强大的实际应用潜力.
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