Compound Autoregressive Network for Prediction of Multivariate Time Series
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 predict...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9107167 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562766880178176 |
|---|---|
| author | Yuting Bai Xuebo Jin Xiaoyi Wang Tingli Su Jianlei Kong Yutian Lu |
| author_facet | Yuting Bai Xuebo Jin Xiaoyi Wang Tingli Su Jianlei Kong Yutian Lu |
| author_sort | Yuting Bai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c4de6c53b3084cc490c64d7e5120d786 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c4de6c53b3084cc490c64d7e5120d7862025-02-03T01:21:50ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/91071679107167Compound Autoregressive Network for Prediction of Multivariate Time SeriesYuting Bai0Xuebo Jin1Xiaoyi Wang2Tingli Su3Jianlei Kong4Yutian Lu5School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaState Grid Beijing Electric Power Company, Beijing 100031, ChinaThe 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.http://dx.doi.org/10.1155/2019/9107167 |
| spellingShingle | Yuting Bai Xuebo Jin Xiaoyi Wang Tingli Su Jianlei Kong Yutian Lu Compound Autoregressive Network for Prediction of Multivariate Time Series Complexity |
| title | Compound Autoregressive Network for Prediction of Multivariate Time Series |
| title_full | Compound Autoregressive Network for Prediction of Multivariate Time Series |
| title_fullStr | Compound Autoregressive Network for Prediction of Multivariate Time Series |
| title_full_unstemmed | Compound Autoregressive Network for Prediction of Multivariate Time Series |
| title_short | Compound Autoregressive Network for Prediction of Multivariate Time Series |
| title_sort | compound autoregressive network for prediction of multivariate time series |
| url | http://dx.doi.org/10.1155/2019/9107167 |
| work_keys_str_mv | AT yutingbai compoundautoregressivenetworkforpredictionofmultivariatetimeseries AT xuebojin compoundautoregressivenetworkforpredictionofmultivariatetimeseries AT xiaoyiwang compoundautoregressivenetworkforpredictionofmultivariatetimeseries AT tinglisu compoundautoregressivenetworkforpredictionofmultivariatetimeseries AT jianleikong compoundautoregressivenetworkforpredictionofmultivariatetimeseries AT yutianlu compoundautoregressivenetworkforpredictionofmultivariatetimeseries |