The Variational Bayesian Inference for Network Autoregression Models
This work addresses computational efficiency in network autoregression models for researchers and practitioners in fields like energy forecasting, though it is incremental as it adapts an existing method to a specific domain.
The authors tackled the problem of estimating large-scale dynamic network models by developing a variational Bayesian approach, which achieved similar or better accuracy than MCMC methods while halving computational time in simulations and provided promising forecasting accuracy in a real-world natural gas flow prediction scenario.
We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework. The VB approach allows for the automatic identification of the dynamic structure of such a model and obtains a direct approximation of the posterior density. Compared to Markov Chain Monte Carlo (MCMC) based sampling approaches, the VB approach achieves enhanced computational efficiency without sacrificing estimation accuracy. In the simulation study conducted here, the proposed VB approach detects various types of proper active structures for dynamic network models. Compared to the alternative approach, the proposed method achieves similar or better accuracy, and its computational time is halved. In a real data analysis scenario of day-ahead natural gas flow prediction in the German gas transmission network with 51 nodes between October 2013 and September 2015, the VB approach delivers promising forecasting accuracy along with clearly detected structures in terms of dynamic dependence.