%0 Journal Article
%T Comparative Analysis of ARIMA and NNAR Models for Time Series Forecasting
%A Ghadah Alsheheri
%J Journal of Applied Mathematics and Physics
%P 267-280
%@ 2327-4379
%D 2025
%I Scientific Research Publishing
%R 10.4236/jamp.2025.131012
%X This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.
%K Time Series
%K QRIMQ Model
%K Neutral Network
%K NNAR Model
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=140204