Learning dependency structure is meaningful to characterize causal or statistical relationships. Traditional dependencies learning algorithms only use the same time stamp data of variables. However, in many real-world applications, such as traffic system and climate, time lag is a key feature of hidden temporal dependencies, and plays an essential role in interpreting the cause of discovered temporal dependencies. In this paper, we propose a method for mining dependencies by considering the time lag. The proposed approach is based on a decomposition of the coefficients into products of two-level hierarchical coefficients, where one represents feature-level and the other represents time-level. Specially, we capture the prior information of time lag in spatio-temporal traffic data.We construct a probabilistic formulation by applying some probabilistic priors to these hierarchical coefficients, and devise an expectation-maximization (EM) algorithm to learn the model parameters.We evaluate our model on both synthetic and real-world highway traffic datasets. Experimental results show the effectiveness of our method.
CITATION STYLE
Zhou, X., Hong, H., Xing, X., Huang, W., Bian, K., & Xie, K. (2015). Mining dependencies considering time lag in spatio-temporal traffic data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9098, pp. 285–296). Springer Verlag. https://doi.org/10.1007/978-3-319-21042-1_23
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