An evaluation of forecasting methods for anticipating spare parts demand.
Keywords
autoregressive moving average processes; feedforward neural nets; maintenance engineering; recurrent neural nets; supply and demand; ARIMA method; artificial neural networks; autoregressive integrated moving average model; demand forecasting methods; feed-forward neural networks; recurrent neural networks; spare parts demand anticipation; Biological system modeling; Computational modeling; Forecasting; Mathematical model; Predictive models; Recurrent neural networks; Reservoirs; ARIMA; Artificial Neural Networks; Croston's Method; Demand Forecas; Reservoir Computing; Spare Parts Demand
Cite as
B., A. d. M. M., D., d. S. B., & B., H. a. F. B. d. L. N. (2015). An evaluation of forecasting methods for anticipating spare parts demand. In Proceedings of the Latin America Congress on Computational Intelligence (LA-CCI), Curitiba, Brazil, 1–6.Details
Publication type
Research article in proceedings (conference)
Peer reviewed
Yes
Publication status
Published
Year
2015
Conference
Latin America Congress on Computational Intelligence (LA-CCI)
Venue
Curitiba, Brazil
Book title
2015 Latin America Congress on Computational Intelligence (LA-CCI)
Start page
1
End page
6
Language
English
DOI