Daily and monthly sugar price forecasting using the mixture of local expert models

Abstract

This article concerns the application of the Mixture of Local Expert Models (MLEM) to predict the daily and monthly price of the Sugar No. 14 contract in the New York Board of Trade. This technique can be seen as a forecasting method that performs data exploratory analysis and mathematical modeling simultaneously. Given a set of data points, the basic idea is as follows: 1) a Kohonen Neural Network is used to divide the data into clusters of points, 2) several modeling techniques are then used to construct competing models for each cluster, 3) the best model for each cluster is then selected and called the Local Expert Model. Finally, a so-called Gating Network combines the outputs of all Local Expert Models. For comparison purposes, the same modeling techniques are also evaluated when acting as Global Experts, i. e., when the technique uses the entire data set without any clustering.Keywords: mixture of local expert models; forecasting time series; neural networks. ResumoEste artigo aborda a aplicação de Modelos da Composição de Especialistas Locais (MCEL) para previsão de preços diários e mensais da comodity açúcar da bolsa de valores de Nova York. Esta técnica pode ser vista como método de previsão que realiza simultaneamente análise exploratória de dados assim como modelagem matemática. Dado um conjunto de dados, a idéia básica é a seguinte: 1) uma Rede Neural de Kohonen é utilizada para dividir o conjunto de dados em clusters, 2) várias técnicas de modelagem são utilizadas para calibrar modelos para cada cluster, 3) o melhor modelo para cada cluster é selecionado e denominado Modelo de Especialista Local. Finalmente, uma Rede Supervisora combina as saídas de cada um dos Modelos de Especialista Local. Com a finalidade de comparação, as mesmas técnicas de modelagem são também avaliadas atuando como Especialistas Globais, ou seja, quando as técnicas usam o conjunto de dados único, sem clusters.Palavras-chave: modelos da composição de especialistas locais; previsão de séries temporais; redes neurais.

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