Time series analysis-based models for predicting utilization of electronic resources

Abstract

Abstract This case study examines the use of electronic resources in academic institutions and the difficulties in forecasting their usage. By employing time series analysis-based models, the study forecasts the utilization of e-resources from 2012 to 2021. It concludes that both Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models successfully predict electronic resource usage. The study emphasizes the significance of precise predictions for academic institution libraries to manage their collections, allocate resources efficiently, and make informed decisions regarding future acquisitions. The research seeks to forecast the trends of e-resource usage for the next decade based on the decade’s data (2012 to 2021).