Forecasting traffic time series with multivariate predicting method

Yi Yin and Pengjian Shang

Applied Mathematics and Computation, 2016, vol. 291, issue C, 266-278

Abstract: Scalar time series considered in most studies may be not sufficient to reconstruct the dynamics, while using multivariate time series may demonstrate great advantages over scalar time series if they are available. Multivariate time series are available in the traffic system and we intend to examine the issue for the real data in the traffic system. In this paper, we propose the multivariate predicting method and discuss the prediction performance of multivariate time series by comparison with univariate time series and K-nearest neighbor (KNN) nonparametric regression model. The three kinds of forecast accuracy measure for multivariate predicting method are smaller than those for the other two methods in all cases, which suggest the predicting results for traffic time series by multivariate predicting method are better and more accurate than those based on univariate time series and KNN model. It demonstrates that the proposed multivariate predicting method is more successful in predicting the traffic time series than univariate predicting method and KNN method. The multivariate predicting method has a broad application prospect on prediction because of its advantage on recovering the dynamics of nonlinear system.

Keywords: Multivariate predicting method; Univariate predicting method; K-nearest neighbor (KNN) nonparametric regression model; Forecast accuracy measure; Traffic time series (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:291:y:2016:i:c:p:266-278

DOI: 10.1016/j.amc.2016.07.017

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