论文标题
时间序列数据的串行相关聚类的自回归混合模型
Autoregressive Mixture Models for Serial Correlation Clustering of Time Series Data
论文作者
论文摘要
聚类时间序列分为相似的组可以通过在类似时间序列中组合信息来改善模型。尽管有一个良好的文献用于时间序列，但这些方法倾向于独立于模型训练而产生簇，这可能会导致模型拟合度不佳。我们提出了一种新颖的分布方法，该方法同时群集和适合相似个体组的自动降压模型。我们应用Wishart混合模型，以同时对相应的自动助矩阵进行建模。拟合的WishArt量表矩阵映射通过Yule-Walker方程式到群集级自回归系数，并适合强大的放热自回归混合模型。这种方法能够辨别出具有较大异质数据集的设置中时间序列的潜在自相关变化的差异。我们证明了群集成员估计器，系数的渐近分布，并通过拟合COVID-19预测模型将我们的方法与竞争方法进行比较。
Clustering time series into similar groups can improve models by combining information across like time series. While there is a well developed body of literature for clustering of time series, these approaches tend to generate clusters independently of model training which can lead to poor model fit. We propose a novel distributed approach that simultaneously clusters and fits autoregression models for groups of similar individuals. We apply a Wishart mixture model so as to cluster individuals while modeling the corresponding autocovariance matrices at the same time. The fitted Wishart scale matrices map to cluster-level autoregressive coefficients through the Yule-Walker equations, fitting robust parsimonious autoregressive mixture models. This approach is able to discern differences in underlying autocorrelation variation of time series in settings with large heterogeneous datasets. We prove consistency of our cluster membership estimator, asymptotic distributions of coefficients and compare our approach against competing methods through simulation as well as by fitting a COVID-19 forecast model.
