用统计和机器学习模型评估环境时间序列的可预测性.
Matthew Bonas1, Abhirup Datta2, Christopher K Wikle3
1Dept. of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
Environmetrics
|February 28, 2025
概括
机器学习和统计模型用于环境预测进行比较. 统计模型提供了正式的不确定性量化,而机器学习在预测准确度方面表现出色,这表明综合方法是最佳的.
科学领域:
- 环境统计环境统计
- 机器学习应用程序 机器学习应用程序
- 科学建模的科学建模
背景情况:
- 机器学习方法在科学学科中越来越受欢迎,包括环境统计.
- 如今,神经网络和决策树等技术已经成为环境过程预测的常用技术.
- 这种趋势挑战了传统的统计建模,需要评估既定方法的作用.
研究的目的:
- 调查环境统计中的统计和机器学习模型的比较性能.
- 评估不同的建模方法的预测技能,不确定性量化和计算效率.
- 为讨论是否应该保留或适应机器学习环境的经典统计方法提供信息.
主要方法:
- 进行了两个时间序列案例研究.
- 采用了来自统计和机器学习文献的精选模型.
- 对比分析侧重于预测准确性,不确定性量化和计算时间.
主要成果:
- 机器学习模型通常表现出卓越的预测能力.
- 统计模型提供了更强大的不确定性量化.
- 计算时间在不同模型类型之间差异很大.
结论:
- 无论是统计还是机器学习方法,对于环境统计都不是普遍优越的.
- 混合方法,利用两者的优势,可能是最有效的解决方案.
- 需要进一步的研究来将基于模型的统计原则与机器学习框架相结合.
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