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TitleTime series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble
Author(s)Peralta Donate, Juan
Cortez, Paulo
Gutierrez Sanchez, German
Sanchis de Miguel, Araceli
Evolutionary computation
Genetic algorithms
Multilayer perceptron
Time series forecasting
Issue dateJun-2013
Abstract(s)The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt–Winters statistical method.
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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