  Prediction of the future performance of academic journals is a task that can
benefit a variety of stakeholders including editorial staff, publishers,
indexing services, researchers, university administrators and granting
agencies. Using historical data on journal performance, this can be framed as a
machine learning regression problem. In this work, we study two such regression
tasks: 1) prediction of the number of citations a journal will receive during
the next calendar year, and 2) prediction of the Elsevier CiteScore a journal
will be assigned for the next calendar year. To address these tasks, we first
create a dataset of historical bibliometric data for journals indexed in
Scopus. We propose the use of neural network models trained on our dataset to
predict the future performance of journals. To this end, we perform feature
selection and model configuration for a Multi-Layer Perceptron and a Long
Short-Term Memory. Through experimental comparisons to heuristic prediction
baselines and classical machine learning models, we demonstrate superior
performance in our proposed models for the prediction of future citation and
CiteScore values.
