Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators

Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators

In the last decade, the size and complexity of datasets have expanded significantly, necessitating more sophisticated predictive methods. Despite this growth, limited research has been conducted on the effects of autocorrelation within widely used regression methods. This study addresses this gap by...

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Bibliographic Details
Main Authors: Rauf I. Rauf, Masad A. Alrasheedi, Rasheedah Sadiq, Abdulrahman M. A. Aldawsari
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Mathematics
Subjects:
autocorrelation
estimators
prediction
ANN
Online Access:https://www.mdpi.com/2227-7390/12/24/3966
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