Compound Autoregressive Network for Prediction of Multivariate Time Series

• Published in: Complexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 11
• Main Authors: Kong, Jianlei, Su, Tingli, Wang, Xiao-yi, Xue-bo, Jin, Bai, Yuting, Lu, Yutian
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Analysis; Artificial intelligence; Autoregressive models; Complex systems; Computer science; Deep learning; Environmental monitoring; Feature extraction; International conferences; Kalman filters; Machine learning; Multivariate analysis; Neural networks; Nonlinear systems; Process controls; Random variables; Time series; Traffic; China
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2019/9107167

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.