Combinação dos modelos de Box-Jenkins e MLP/RNA para previsão de séries temporais
(Combination of Box-Jenkins and MLP/RNA models for forecasting)
William Jacobs (firstname.lastname@example.org)1, Adriano Mendonça Souza (email@example.com)2, Roselaine Ruviaro Zanini (firstname.lastname@example.org)2
1Centro Universitário Univates2Universidade Federal de Santa Maria
This paper appears in: Revista IEEE América Latina
Publication Date: April 2016
Volume: 14, Issue: 4
This study aims to predict the values of the time series of UHT milk demand in a dairy industry by combining forecasting of ARIMA and MLP/RNA models and compare the results to the individual models, exemplifying the combined forecast for the production planning. Eight predictions combining techniques were used and, after the use of statistical techniques, the results obtained by fitting the ARIMA and MLP/RNA templates were compared with the results obtained in the proposed combinations. The results showed that the combination of SARIMA models (3,0,1)(1,1,0)12 and DMLP the inverse mean square method provided a performance in the forecast, for six months ahead, 66.5% higher than the individual models where the combination of forecasts provided a RMSE of 1.43 and MAPE of 2.16. The forecast for 12 months on, the performance of the combination was 56.5% higher compared to individual models, with RMSE of 2.86 and MAPE of 3.70%. In both cases, the combination of predictions showed superior results.
Demand Forecast, Forecast Combination, ARIMA Model, Artificial Neural Network, Multilayer Perceptron.
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