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- W2536690300 abstract "Artificial neural networks (ANN) have been paramount for modeling and forecasting time series phenomena. In this way it has been usual to suppose that each ANN model generates a white noise as prediction error. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. On the other hand, to adopt a single ANN model may lead to statistical bias and underestimation of uncertainty. The present paper introduces a two-step maximum likelihood method for correcting and combining ANN models. Applications involving single ANN models for Dow Jones Industrial Average Index and S&P500 series illustrate the usefulness of the proposed framework." @default.
- W2536690300 created "2016-10-28" @default.
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- W2536690300 date "2013-09-01" @default.
- W2536690300 modified "2023-09-26" @default.
- W2536690300 title "Combination of Biased Artificial Neural Network Forecasters" @default.
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- W2536690300 doi "https://doi.org/10.1109/brics-cci-cbic.2013.92" @default.
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