Combinação das previsões dos modelos de Box-Jenkins e MLP/RNA para a previsão de demanda no planejamento da produção

• Autor(a) principal: Jacobs, William
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional Manancial UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/8327
• topic: Previsão de demanda; Combinação de previsões; Modelos ARIMA; Redes neurais artificiais; Planejamento e controle da produção; Forecasting; Forecasting combination; ARIMA models; Artificial neural networks; Production planning and controlling; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO

A forecast of future demand for the products is the main variable to be considered in the planning and in production control in organizations. Two methods of time series forecasting often used in the literature are the ARIMA and MLP/RNA models. A practice that began in 1969 and has consolidated for greater accuracy is the combination of individual forecasts from two or more models. Considering the need for organizations by predictive techniques that generate better results, this study aims to predict the future values of a time series of the demand for UHT milk in a dairy industry, through the combination of ARIMA and MLP/RNA models, and to compare the results obtained by the combinations compared to individual models, exemplifying the achievement of combined forecasting in production planning. Accuracy measures to measure the results and to select the best model were the RMSE and MAPE for forecasting. The results showed that the combination of models SARIMA(3,0,1)(1,1,0)12 and DMLP the inverse mean square method provided a performance forecast for the six months ahead, up to 66.5% higher than individual models used, where the combination of the predictions obtained a RMSE of 1.43, and a MAPE of 2.16. In the 12 month ahead prediction for the performance of the combination was up to 56.5% higher compared to individual models, in which case obtained a RMSE of 2.86 and 3.70% MAPE. The combination of time series models enabled a significant increase in performance prediction models, but in order to produce satisfactory absolute results should be used to complement their predictive abilities mutually.