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There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time\u2010series impact on each other to make the prediction more difficult. Then, a solution of time\u2010series 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\u2010monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.<\/jats:p>","DOI":"10.1155\/2019\/9107167","type":"journal-article","created":{"date-parts":[[2019,9,22]],"date-time":"2019-09-22T23:30:47Z","timestamp":1569195047000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Compound Autoregressive Network for Prediction of Multivariate Time Series"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8047-1010","authenticated-orcid":false,"given":"Yuting","family":"Bai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-0077","authenticated-orcid":false,"given":"Xuebo","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2140-6782","authenticated-orcid":false,"given":"Xiaoyi","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-1702","authenticated-orcid":false,"given":"Tingli","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0074-3467","authenticated-orcid":false,"given":"Jianlei","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Yutian","family":"Lu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,9,22]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0152491"},{"key":"e_1_2_10_2_2","first-page":"283","article-title":"The EFDC model integration and application in the Three Gorges reservoir","volume":"31","author":"Liu X.","year":"2018","journal-title":"Research of Environmental Sciences"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijep.2008.021135"},{"volume-title":"Time Series Analysis: Forecasting and Control","year":"1976","author":"Borrego G. 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