Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming

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ID: 69819
2016
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Abstract
This paper proposes a linear combination of forecasts obtained from three forecasting methods (namely, ARIMA, Exponential Smoothing and Artificial Neural Networks) whose adaptive weights are determined via a multi-objective non-linear programming problem, which seeks to minimize, simultaneously, the statistics: MAE, MAPE and MSE. The results achieved by the proposed combination are compared with the traditional approach of linear combinations of forecasts, where the optimum adaptive weights are determined only by minimizing the MSE; with the combination method by arithmetic mean; and with individual methods
Reference Key
corra2016lineargepros Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Corrêa, Jairo Marlon;Neto, Anselmo Chaves;Júnior, Luiz Albino Teixeira;Carreño, Edgar Manuel;Faria, Álvaro Eduardo;
Journal gepros: gestão da produção, operações e sistemas
Year 2016
DOI
DOI not found
URL
http://revista.feb.unesp.br/index.php/gepros/article/view/1322/703
Keywords
Chemical technology Science environmental effects of industries and plants renewable energy sources environmental sciences physical geography mathematics industrial engineering. management engineering

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