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基于LSTM和ARIMA模型预测和分析全球平均温度趋势
Can Tan1,2, Junyi Zhong3, Dajun Yang4
1School of Management, Guangdong Ocean University, Zhanjiang City, Guangdong Province, China.
PloS one
|September 3, 2025
概括
全球变暖趋势显著,但COVID-19暂时减缓了温室气体排放. 这项研究结合了LSTM和ARIMA模型来准确预测全球温度,帮助环境政策.
科学领域:
- 气候科学
- 时间序列分析
- 环境科学
背景情况:
- 全球平均温度与温室气体之间确立了相关性.
- 为了提高温度预测的准确性,使用LSTM和ARIMA模型进行有限的探索.
- 关于COVID-19对全球平均温度的影响的假设.
研究的目的:
- 调查LSTM和ARIMA模型在全球温度预测方面的联合潜力.
- 评估COVID-19对全球平均温度趋势的影响.
- 提供更准确,更全面的温度预测方法.
主要方法:
- 利用从1880年到2022年的全球平均温度数据.
- 长期依赖的联合长期短期记忆 (LSTM) 模型与线性时间序列数据的自回归集成移动平均 (ARIMA) 模型.
- 采用混合模式来利用LSTM和ARIMA的优势.
主要成果:
- 证实了全球气温上升的早期趋势,
- 发现COVID-19间接减少了温室气体排放,减轻了全球变暖.
- 观察到经度和平均温度之间的弱相关性,以及度和温度之间的强烈负相关性.
结论:
- 混合LSTM-ARIMA模型为全球温度预测提供了更准确和更全面的方法.
- COVID-19 对排放的影响表明全球变暖暂时放缓.
- 结果为生态治理和经济政策制定提供了关键的见解.
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