Sparse seasonal and periodic vector autoregressive modeling
Sparse seasonal and periodic vector autoregressive modeling
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稀疏季节性和周期性向量自回归建模
DOI：
10.1016/j.csda.2016.09.005
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
V. Pipiras
中科院分区：
文献类型：
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作者：
Changryong Baek;R. Davis;V. Pipiras
季节向量自回归和周期向量自回归是建模具有周期性变化的向量时间序列的两种常用方法。这些模型中的参数总数随着模型的维度和阶数的增加而迅速增加，使得对模型的解释变得困难，参数估计的稳定性也受到质疑。为了解决这些和其他问题，本工作提出了两种稀疏建模方法：第一，基于涉及自适应套索的正则化；第二，扩展了Davis等人的方法。(2015)关于基于部分谱相关的向量自回归。这些方法在模拟数据上表现得很好，对几个表现出周期性变化的真实向量时间序列的例子也表现得很好。
Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies for sparse modeling are presented in this work: first, based on regularization involving adaptive lasso and, second, extending the approach of Davis et al. (2015) for vector autoregressions based on partial spectral coherences. The methods are shown to work well on simulated data, and to perform well on several examples of real vector time series exhibiting cyclical variations.