A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy

Abstract

Faced with the high-dimensional characteristics of time series, symbolization is usually used to reduce the dimensionality and effectively eliminate redundancy and irrelevant features. Several existing network prediction methods, which based on the topology of complex networks, only consider the target sequence itself, which ignored the influence of related factors. In this paper, we improve the single-layer network and construct a Multi-layer Symbolic Pattern Network (MSPN) prediction model by considering the interaction between different variables with time lag. The basic idea is to link the directed weighted networks based on causation entropy (CSE), then extract the multi-layer network topology to jointly predict the target sequence. The 2018/1–2018/12 fluctuation trend of crude oil futures prices are predicted, with auxiliary variables including crude oil supply, demand and the three major U.S. stock indexes (S&P 500 Index, NASDAQ Composite Index, and Dow Jones Industrial Average). The results show that the multi-layer network improves the directional prediction accuracy of oil price over the single-layer network, where demand and Dow Jones Industrial Average’s auxiliary perform best. It is worth noting that considering auxiliary variables can be effective in improving the accuracy of the model. The more auxiliary layers are not the better, generally considering two auxiliary layers, that is, the three-layer network prediction model can achieve higher accuracy.