The time dependency predictive model on the basis of community detection and long‐short term memory. (20th June 2017)

• Record Type: Journal Article
• Authors: Zhang, Gaowei; Xu, Lingyu; Xue, Yunlan
• Other Names: Carretero Jesus guestEditor.; Garcia‐Blas Javier guestEditor.; Nakano Koji guestEditor.; Mueller Peter guestEditor.; Grosu Daniel guestEditor.; Zheng Sheng guestEditor.; Xu Li guestEditor.; Xu Zheng guestEditor.; Yen Neil guestEditor.; Sugumaran Vijayan guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 24(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-20
• Subjects: cluster -- community detection -- complexity network -- neural networks; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4184
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: The construction of complex networks in current financial data does not take into account the energy characteristics that exist in similar stocks. Without considering the stock network as a kind of asymmetric directed network, most of the existing researches only measure the importance of nodes from the nodes degree in the network, lack of comprehensive consideration of structure and function. Second, for the division of the community, many algorithms are based on the number of community is known, and the network is unweighted and undirected. Based on the entropy analysis of information theory, in this paper we study the complexity and information flow of stock time series and construct the stock influence network on the basis of transfer entropy. By combining the structural features and functional characteristics of complex networks, we find the relationship between stocks. At last we design and verify the forecasting ability of the proposed long‐ and short‐memory model.