概率性太阳黑子预测与一个基于反复单位的结合模型,以球损失为指导
Zhesen Cui1, Zhe Ding2, Jing Xu3
1Department of Computer Science, Changzhi University, Changzhi, 046011, People's Republic of China.
Scientific reports
|June 12, 2024
概括
这项研究引入了使用先进的循环神经网络预测太阳黑子的新模型,以及用于更准确的概率预测的新浪损失函数,改进了太阳风暴监测.
科学领域:
- 太阳物理 太阳物理
- 太空天气 太空天气
- 时间序列分析时间序列分析.
背景情况:
- 太阳黑子对于天气预报和太阳风暴监测至关重要.
- 准确的太阳黑子预测对于太空天气研究至关重要.
- 现有的模型面临着序列预测复杂性的挑战.
研究的目的:
- 开发一种用于增强太阳黑子预测的新型组合模型.
- 为了结合使用平球损失的概率预测.
- 提高太阳黑子预测的准确性和可靠性.
主要方法:
- 为了序列预测,利用了改进的封闭循环单位 (GRU).
- 优化了GRU参数,使用了粘液模具算法.
- 采用季节性趋势分解,使用loess对非静止数据.
- 实现了用于概率量子预测的球损失.
主要成果:
- 拟议的模型在太阳黑子值预测方面表现出卓越的性能.
- 实现了准确的单步和多步预测.
- 在历史数据上表现优于现有的太阳黑子预测方法.
结论:
- 这种新型的组合模型有效地预测了太阳黑子值,准确度提高了.
- 使用球损失进行概率预测,提高了预测可靠性.
- 该模型在太空天气预报能力方面取得了重大进展.
相关概念视频
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