Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Abstract

Unique visitors are first-time IP address visitors in a certain time window, a significant indicator of an electronic journal's performance and accreditation. This study uses a backpropagation neural network (BPNN) to improve visitor prediction. From January 1, 2018, to December 31, 2018, the KEDS.csv file on the page contained page views, sessions, visitors, and new visitors. The data is preprocessed using mean and median smoothing. MSE and RMSE are examined and compared to the BPNN model without smoothing and the original data. The BPNN model with mean smoothing, MSE 0.00129, and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture has the lowest error. Integrating mean and median smoothing techniques considerably enhances the BPNN forecasting model. Mean, and median smoothing lowers data volatility and noise, making predictions more accurate. The BPNN model with smoothing has lower MSE and RMSE than the model without smoothing and the original data. This work is unusual in combining mean and median smoothing with a BPNN model to predict unique visits to electronic journals. This research advances time series forecasting by predicting electronic journal visiting patterns. The literature benefits from evaluating smoothing strategies and their effects on predicting. The study helps electronic journal practitioners evaluate visitor patterns and journal performance by boosting prediction accuracy.