Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
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Optimization: algorithms and consistent approximations
Selection of relevant features and examples in machine learning
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Feedforward Neural Network Methodology
Feedforward Neural Network Methodology
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A non-parametric learning algorithm for small manufacturing data sets
Expert Systems with Applications: An International Journal
Nonlinear time series modeling and prediction using local variable weights RBF network
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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In statistics, Box-Jenkins Time Series is a linear method widely used to forecasting. The linearity makes the method inadequate to forecast real time series, which could present irregular behavior. On the other hand, in artificial intelligence FeedForward Artificial Neural Networks and Continuous Machine Learning Systems are robust handlers of data in the sense that they are able to reproduce nonlinear relationships. Their main disadvantage is the selection of adequate inputs or attributes better related with the output or category. In this paper, we present a methodology that employs Box-Jenkins Time Series as feature selector to Feedforward Artificial Neural Networks inputs and Continuous Machine Learning Systems attributes. We also apply this methodology to forecast some real time series collected in a power plant. It is shown that Feedforward Artificial Neural Networks performs better than Continuous Machine Learning Systems, which in turn performs better than Box-Jenkins Time Series.