Development of High-dimensional Data Sparse Modeling in Data Space and Its Application in Manufacturing Multi-value Chain Collaboration

Abstract

The arrival of data space marks that all kinds of data applications can be recorded, stored and continuously expanded. The changes of data's source, volume and type have been increasing the difficulty of statistical analysis undoubtedly. In order to cope with the high-dimensional characteristics of data in data space, this paper discusses the challenges brought by high-dimensional data and high-dimensional models to traditional methods. The development of sparse modeling, the role of selection mechanism and the theoretical nature of penalty function method have also been combed in this paper. Finally, as an application, this paper discusses the feasibility of using high-dimensional sparse vector autoregressive model (HDS-VAR) to predict the profitability of manufacturing enterprises represented by electrical machinery and equipment manufacturing enterprises under the synergy of manufacturing value chain and service value chain.