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.


Schlüsselwörter
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



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2015

Konferenz
Latin America Congress on Computational Intelligence (LA-CCI)

Konferenzort
Curitiba, Brazil

Buchtitel
2015 Latin America Congress on Computational Intelligence (LA-CCI)

Seiten
1-6

DOI