Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Expert Systems with Applications: An International Journal
Reliability forecasting by recurrent Support Vector Regression
International Journal of Artificial Intelligence and Soft Computing
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Recurrent support vector machines in reliability prediction
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Expert Systems with Applications: An International Journal
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
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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.