稀疏识别和估计大规模矢量自回归移动平均线的稀疏识别和估计
Ines Wilms1, Sumanta Basu2, Jacob Bien3
1Department of Quantitative Economics, Maastricht University, Maastricht, The Netherlands.
本研究介绍了向量自回归移动平均 (VARMA) 模型的新优化方法,解决了可识别性问题. 该方法使用凸优化来找到最简单的模型,使VARMA在时间序列分析中变得更加实用.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 矢量自回归移动平均 (VARMA) 模型对于多变量时间序列分析至关重要.
- 可识别性挑战在历史上限制了VARMA模型的采用,有利于更简单的矢量自回归 (VAR) 模型.
- 现有的方法经常与VARMA模型的复杂性和可解释性作斗争.
研究的目的:
- 为VARMA模型识别开发一种基于优化的新方法.
- 为了弥合理论VARMA模型和实际时间序列分析之间的差距.
- 为了提高VARMA模型估计的节性和效率.
主要方法:
- 利用凸优化来识别同等数据生成模型中最节的参数化.
- 采用用户指定的强凸处罚来量化模型的简单性.
- 根据所选择的处罚,开发了一个高效计算的估计器.
主要成果:
- 在双非对称模式下建立了拟议估计者的一致性.
- 提供了涵盖模型规范和参数估计的非对称误差边界.
- 通过使用三个真实世界的数据集,证明了该方法对传统VAR方法的优势.
结论:
- 提出的基于优化的方法有效地解决了VARMA的识别问题.
- 这种方法为现有的时间序列模型提供了更实用和节的替代方案.
- 这些发现对大规模时间序列算法和惩罚回归分析有影响.
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