Learning dependency structure is meaningful to characterize causal or statistical relationships. Traditional dependencies learning algo- rithms 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.
['Xiabing Zhou', 'Haikun Hong', 'Xingxing Xing', 'Wenhao Huang', 'Kaigui Bian', 'Kunqing Xie']
Mining Dependencies Considering Time Lag in Spatio-Temporal Traffic Data