Weighted Cross-Validation Evolving Artificial Neural Networks to Forecast Time Series
Accurate time series forecasting is a key tool to support decision making and for planning our day to-day activities. In recent years, several works in the literature have adopted evolving artificial neural networks (EANN) for forecasting applications. EANNs are particularly appealing due to their ability to model an unspecified non-linear relationship between time series variables. In this work, a novel approach for EANN forecasting systems is proposed, where a weighted cross-validation is used to build an ensemble of neural networks. Several experiments were held, using a set of six real-world time series (from different domains) and comparing both the weighted and standard cross-validation variants. Overall, the weighted cross-validation provided the best forecasting results.
KeywordsEvolutionary Computation Genetic Algorithms Artificial Neural Networks Time Series Forecasting Ensembles
Unable to display preview. Download preview PDF.
- 1.Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting methods and applications. John Wiley & Sons, USA (2008)Google Scholar
- 2.Nunn, I., White, T.: The application of antigenic search techniques to time series forecasting. In: Proceedings of GECCO, pp. 353–360. ACM, New York (2005)Google Scholar
- 4.Cortez, P., Rocha, M., Neves, J.: Time Series Forecasting by Evolutionary Neural Networks. In: Rubuñal, J., Dorado, J. (eds.) Artificial Neural Networks in Real-Life Applications, ch. III, pp. 47–70. Idea Group Publishing, Hershey (2006)Google Scholar
- 5.Peralta, J., Li, X., Gutierrez, G., Sanchis, A.: Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution. In: Proceedings of IJCNN, pp. 3999–4006. IEEE, Los Alamitos (2010)Google Scholar
- 7.Yao, X.: Evolving Artificial Neural Networks. Proceedings of IEEE 9(87), 1423–1447 (2002)Google Scholar
- 8.Abraham, A.: Meta-Learning Evolutionary Artificial Neural Networks. Neurocomputing 56(c), 1–38 (2004)Google Scholar
- 11.Fogel, D.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Wiley-IEEE Press (1998)Google Scholar
- 12.Mosteller, F.: A k-sample slippage test for an extreme population. Annals of Mathematical Statistics, 101–109 (2006)Google Scholar
- 14.Wah, B., Qian, M.: Time-series predictions using constrained formulations for neural-network training and cross validation. In: Proc. of 16th Int. Conf. on Intelligent Information Processing, pp. 220–226. Kluwer Academic Press, Dordrecht (2000)Google Scholar
- 18.Hyndman, R.: Time series data library, http://robjhyndman.com/TSDL/ (accessed September 2010)