使用基于巴林年度时间序列数据的混合SSA ARIMA模型改进了二氧化碳排放的预测
1Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, 71491, Saudi Arabia. zalthebiti@ut.edu.sa.
Scientific reports
|July 16, 2025
概括
预测巴林的二氧化碳 (CO2) 排放对可持续性至关重要. 一个新的混合模型结合了单点频谱分析 (SSA) 和自动回归集成移动平均线 (ARIMA) 显著提高了比传统方法的准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 准确的二氧化碳 (CO2) 排放预测对于环境可持续性至关重要,特别是在像巴林这样依赖化石燃料的国家.
- 现有的预测模型可能无法充分捕捉巴林的特定排放模式,需要先进的分析工具.
- 关于巴林二氧化碳排放趋势的先前研究有限,突出了需要新型建模方法的需求.
研究的目的:
- 开发和验证巴林年度二氧化碳排放的混合预测模型.
- 通过将单点光谱分析 (SSA) 与自动回归集成移动平均线 (ARIMA) 集成,提高二氧化碳排放预测的准确性.
- 为决策者提供一个可靠的工具,以应对巴林的环境压力.
主要方法:
- 开发了一种混合模型,将单元光谱分析 (SSA) 用于时间序列分解和自动回归集成移动平均值 (ARIMA) 用于组件预测.
- SSA将年度二氧化碳排放数据分解为趋势,周期和噪声组件.
- 对每个组件都应用了ARIMA,并使用平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE) 和Diebold-Mariano (DM) 测试来评估模型性能.
主要成果:
- 混合SSA-ARIMA模型在预测二氧化碳排放方面表现优于传统ARIMA模型.
- 在2014-2018年期间,SSA-ARIMA模型实现的MAPE值明显低于ARIMA (例如2.14%).
- 统计测试,包括Diebold-Mariano测试,证实了混合模型提供的预测准确度的显著改善.
结论:
- 混合SSA-ARIMA模型是预测巴林二氧化碳排放的强大而准确的工具.
- 这种先进的预测能力可以支持巴林的环境可持续性倡议和政策制定.
- 该研究强调了混合建模技术在复杂环境数据分析中的价值.
关键词:
巴林 巴林巴林巴林巴林是什么意思在二氧化碳排放方面,二氧化碳排放量Diebold玛丽亚诺的测试环境建模环境建模混合动力SSA-ARIMA是什么意思混合预测模型的混合预测模型.时间序列分析时间序列分析.更多相关视频
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