Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble

Authors:
Juan Peralta Donate;Paulo Cortez;GermáN GutiéRrez SáNchez;Araceli Sanchis De Miguel
Affiliations:
Computer Science Department, Carlos III University, Avda. de la Universidad, 30. 28911 Legans, Madrid, Spain;Centro Algoritmi, Departamento de Sistemas de Informação, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal;Computer Science Department, Carlos III University, Avda. de la Universidad, 30. 28911 Legans, Madrid, Spain;Computer Science Department, Carlos III University, Avda. de la Universidad, 30. 28911 Legans, Madrid, Spain
Venue:
Neurocomputing
Year:
2013

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Abstract

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.