Time Series Prediction Based on Complex-Valued S-System Model
Joint Authors
Chen, Yue-Hui
Yang, Bin
Bao, Wenzheng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-28
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Symbolic regression has been utilized to infer mathematical formulas in order to solve the complex prediction and classification problems.
In this paper, complex-valued S-system model (CVSS) is proposed to predict real-valued time series data.
In a CVSS model, input variables and rate constants are complex-valued.
The time series data need to be translated into complex numbers.
The hybrid evolutionary algorithm based on complex-valued restricted additive tree and firefly algorithm is proposed to search the optimal CVSS model.
Three financial time series data and Mackey–Glass chaos time series are collected to evaluate our proposed method.
The experiment results show that the predicted data are very close to the target ones and our method could obtain the better RMSE, MAP, MAPE, POCID, R2, and ARV performances than ARIMA, radial basis function neural network (RBFNN), flexible neural tree (FNT), ordinary differential equation (ODE), and S-system.
American Psychological Association (APA)
Yang, Bin& Bao, Wenzheng& Chen, Yue-Hui. 2020. Time Series Prediction Based on Complex-Valued S-System Model. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142880
Modern Language Association (MLA)
Yang, Bin…[et al.]. Time Series Prediction Based on Complex-Valued S-System Model. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1142880
American Medical Association (AMA)
Yang, Bin& Bao, Wenzheng& Chen, Yue-Hui. Time Series Prediction Based on Complex-Valued S-System Model. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142880
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142880