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论文类型：期刊论文

发表时间：2016-12-01

发表刊物：JOURNAL OF APPLIED STATISTICS

收录刊物：SCIE、Scopus

卷号：43

期号：16

页面范围：2897-2909

ISSN号：0266-4763

关键字：Vector autoregressive moving average models; Bayesian networks; K-means clustering; hybrid models; multivariate time series forecasting; 62H12; 62P20; C32

摘要：In this paper, a new hybrid model of vector autoregressive moving average (VARMA) models and Bayesian networks is proposed to improve the forecasting performance of multivariate time series. In the proposed model, the VARMA model, which is a popular linear model in time series forecasting, is specified to capture the linear characteristics. Then the errors of the VARMA model are clustered into some trends by K-means algorithm with Krzanowski-Lai cluster validity index determining the number of trends, and a Bayesian network is built to learn the relationship between the data and the trend of its corresponding VARMA error. Finally, the estimated values of the VARMA model are compensated by the probabilities of their corresponding VARMA errors belonging to each trend, which are obtained from the Bayesian network. Compared with VARMA models, the experimental results with a simulation study and two multivariate real-world data sets indicate that the proposed model can effectively improve the prediction performance.