Leveraging temporal autocorrelation of historical data for improving accuracy in network regression

Temporal data describe processes and phenomena that evolve over time. In many real-world applications temporal data are characterized by temporal autocorrelation, which expresses the dependence of time-stamped data over a certain a time lag. Often such processes and phenomena are characterized by evolving complex entities, which we can represent with evolving networks of data. In this scenario, a task that deserves attention is regression inference in temporal network data. In this paper, we investigate how to improve the predictive inference on network data by accommodating temporal autocorrelation of the historical data in the learning process of the prediction models. Historical data is a type of temporal data where most part of the elements has been already stored. In practice, we study how to explicitly consider the influence of data of a network observed in the past, to enhance the prediction on the same network observed at the present. The proposed approach relies on a model ensemble built with individual predictors learned on historical network data. The predictors are trained from summary networks, which synthesize the effect of the autocorrelation in distinct sequences of network observations. Summary networks are identified with a sliding window model. Finally, the model ensemble combines together the predictors with a weighting schema, which reflects the degree of influence of a predictor with respect to the network observed at the present. So, we aim at accommodating the temporal autocorrelation both in the data and in the prediction model. Empirical evaluation demonstrates that the proposed approach can boost regression performance in real-world network data.