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The New England Journal of Statistics in Data Science


Dynamic Continuous Flows on Networks
Volume 1, Issue 1 (2023), pp. 62–68
Pub. online: 25 April 2022      Type: Methodology Article      Open accessOpen Access
Area: Engineering Science
Accepted
7 March 2022
Published
25 April 2022

Abstract

There are many cases in which one has continuous flows over networks, and there is interest in predicting and monitoring such flows. This paper provides Bayesian models for two types of networks—those in which flow can be bidirectional, and those in which flow is unidirectional. The former is illustrated by an application to electrical transmission over the power grid, and the latter is examined with data on volumetric water flow in a river system. Both applications yield good predictive accuracy over short time horizons. Predictive accuracy is important in these applications—it improves the efficiency of the energy market and enables flood warnings and water management.

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© 2023 New England Statistical Society
Open access article under the CC BY license.

Keywords
Autoregressive models Bayesian hierarchical models Network flows

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