This paper presents a new and novel hybrid modeling method for the segmentation of high dimensional time-series data using the mixture of the sparse principal components regression (MIX-SPCR) model with information complexity (ICOMP) criterion as the fitness function. Our approach encompasses dimension reduction in high dimensional time-series data and, at the same time, determines the number of component clusters (i.e., number of segments across time-series data) and selects the best subset of predictors. A large-scale Monte Carlo simulation is performed to show the capability of the MIX-SPCR model to identify the correct structure of the time-series data successfully. MIX-SPCR model is also applied to a high dimensional Standard & Poor’s 500 (S&P 500) index data to uncover the time-series’s hidden structure and identify the structure change points. The approach presented in this paper determines both the relationships among the predictor variables and how various predictor variables contribute to the explanatory power of the response variable through the sparsity settings cluster wise.

中文翻译：

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使用具有信息复杂性的稀疏主成分回归模型的混合来分割高维时间序列数据


本文提出了一种新颖的混合建模方法，使用稀疏主成分回归（MIX-SPCR）模型与信息复杂性（ICOMP）准则的混合作为适应度函数来分割高维时间序列数据。我们的方法包括高维时间序列数据的降维，同时确定组件簇的数量（即跨时间序列数据的分段数量）并选择最佳的预测子集。进行大规模蒙特卡洛模拟以展示 MIX-SPCR 模型成功识别时间序列数据的正确结构的能力。 MIX-SPCR模型还应用于高维标准普尔500（S&P 500）指数数据，以揭示时间序列的隐藏结构并识别结构变化点。本文提出的方法确定了预测变量之间的关系，以及各种预测变量如何通过稀疏设置聚类对响应变量的解释力做出贡献。