A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction

This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network archit...

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Bibliographic Details
Published in:Computers & industrial engineering 2002-06, Vol.42 (2), p.371-375
Main Authors: Ho, S.L, Xie, M, Goh, T.N
Format: Article
Language:English
Subjects:
Applied sciences
Box-Jenkins autoregressive integrated moving average model
Comparative analysis
Exact sciences and technology
Failure analysis
Mathematical models
Multi-layer feed-forward neural network
Neural networks
Operational research and scientific management
Operational research. Management science
Recurrent neural network
Reliability theory. Replacement problems
Simulation
Studies
Time series
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Summary:This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network architectures evaluated are the multi-layer feed-forward network and the recurrent network. Simulation results on a set of compressor failures showed that in modeling the stochastic nature of reliability data, both the ARIMA and the recurrent neural network (RNN) models outperform the feed-forward model; in terms of lower predictive errors and higher percentage of correct reversal detection. However, both models perform better with short term forecasting. The effect of varying the damped feedback weights in the recurrent net is also investigated and it was found that RNN at the optimal weighting factor gives satisfactory performances compared to the ARIMA model.
ISSN:0360-8352
1879-0550
DOI:10.1016/S0360-8352(02)00036-0