An evaluation of forecasting methods for anticipating spare parts demand.

B. A. de Melo Menezes, D. de Siqueira Braga, B. Hellingrath and F. B. de Lima Neto.


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



Publication type
Conference Paper

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)

Pages range
1-6

DOI