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Crone, S.F. and Kourentzes, N. (2011) Neural Networks for Time Series Forecasting: Practical Implications of Theoretical Model Complexity. 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, 31 July-5 August 2011, 2159-2166.

has been cited by the following article:

• TITLE: Comparative Analysis of ARIMA and NNAR Models for Time Series Forecasting

  AUTHORS: Ghadah Alsheheri

  KEYWORDS: Time Series, QRIMQ Model, Neutral Network, NNAR Model

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.1, January 26, 2025

  ABSTRACT: 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.