Thursday 28th of March 2024
 

Comparative Study of Inflation Rates Forecasting Using Feed-Forward Artificial Neural Networks and Auto Regressive Models


Onimode, Bayo Mohammed and Onimode, Bayo Mohammed

This paper examines the efficacy of neural networks application for inflation forecasting. In a simulated out-of-model forecasting investigation using recent Nigeria inflation rate data obtained from the appropriate authorities, the neural networks did better than univariate autoregressive models on normal rate for short periods of quarter one and quarter two; quarter one and quarter three; and quarter one and quarter four. A clear-cut condition of the model of neural network and specialized evaluation trial from the neural networks literature exemplify the important roles in the achievement of the feed-forward neural network model.

Keywords: Inflation, Forecasting, Neural Networks, Feed-forward, Model Selection, Linearity, Forecasting.

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ABOUT THE AUTHORS

Onimode, Bayo Mohammed
Department of Computer Science Federal University of Technology, Minna, Niger State, Nigeria

Onimode, Bayo Mohammed
Bayo Mohammed Onimode is of the Department of Computer Science, in the School of Information and Communication Technology, Federal University of Technology, Minna, Niger State, Nigeria. He obtained a B.Sc Computer Science at the University of Jos, Jos - Nigeria, and a Master of Technology Degree in Computer Science at the Federal University of Technology, Minna. His research has focused on Enterprise Database Management; Management Information Systems; Networks and Computer Security Models; Cyber Security Systems and Health Informatics. He can be reached by phone on +2348036243853 and through E-mail bayonimode@yahoo.co.uk.


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