Work

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments

Year: 2025

Type: article

Abstract: Poisson autoregressive count models have evolved into a time series staple for correlated count data. This paper proposes an alternative to Poisson autoregressions: count echo state networks. Echo state networks can be statistically analyzed in frequentist manners via optimizing penalized likelihoods, or in Bayesian manners via MCMC sampling. This paper develops Poisson echo state techniques for count data and applies them to a count data set containing the number of graduate students from 1,758 United States universities during the years 1972–2021 inclusive. Negative binomial models are also implemented and generally better handle the overdispersion in the counts. Performance of the proposed models are compared via their forecasting performance as judged by several methods. In the end, a hierarchical negative binomial based echo state network is judged as the most appropriate model for graduate student count modeling.

Source: Data Science in Science

DOI: 10.1080/26941899.2025.2565243

Authors: Qi Wang, Paul A. Parker, Robert Lund

Institution: University of California, Santa Cruz

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, National Stroke Foundation

Awards: General Correlated Count Statistical Structures, Collaborative Research: Randomization Based Machine Learning Methods in a Bayesian Model Setting for Data From a Complex Survey or Census