在因果条件转移下可转移时间序列预测
IEEE transactions on pattern analysis and machine intelligence
|August 11, 2023
概括
本研究引入了一种在时间序列预测中进行半监督域调整的新方法. 该方法利用因果结构来提高跨领域预测任务的准确性和可解释性.
科学领域:
- 机器学习 机器学习
- 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:
- 半监督域适应时间序列预测是一个未被充分探索但实际的问题.
- 对于静态数据的现有方法无法捕捉时间序列数据中的复杂依赖关系,例如数据偏移,时间滞后和分布转移.
- 这些局限性阻碍了跨不同数据领域的准确预测.
研究的目的:
- 为了应对半监督领域适应时间序列预测的挑战.
- 提出一种新的端到端模型,考虑域特定的条件依赖关系.
- 提高跨领域场景中的时间序列预测的准确性和可解释性.
主要方法:
- 时间序列数据中的变量条件依赖关系的分析.
- 基于跨领域稳定的因果结构的因果条件转移假设的制定.
- 开发一个端到端模型,将时间序列数据的因果生成过程纳入其中.
- 在跨域数据中发现格兰杰因果结构.
主要成果:
- 拟议的方法有效地解决了用于时间序列预测的半监督域调整问题.
- 发现格兰杰因果结构并进行准确,可解释的跨领域预测的能力.
- 理论分析限制了概括错误的经验风险和因果结构差异.
- 在合成和现实世界数据集上的实验验证证证了该方法的有效性.
结论:
- 提出的基于因果关系的方法显著推进了半监督域适应时间序列预测.
- 该方法通过利用稳定的因果关系,为处理域移动提供了强大的解决方案.
- 未来的工作可以探索因果推理在时间序列建模中的进一步应用.
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