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Forecasting seasonal time series with computational intelligence: on recent met...
Stepnicka, M.; Cortez, Paulo, 1971-; Peralta Donate, Juan; Stepnickova, LenkaAccurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods ...
Time series forecasting using a weighted cross-validation evolutionary artifici...
Peralta Donate, Juan; Cortez, Paulo, 1971-; Gutierrez Sanchez, German; Sanchis de Miguel, AraceliThe 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 w...
Evolutionary support vector machines for time series forecasting
Cortez, Paulo, 1971-; Peralta Donate, JuanAbstract. Time Series Forecasting (TSF) uses past patterns of an event in order to predict its future values and is a key tool to support decision making. In the last decades, Computational Intelligence (CI) techniques, such as Artificial Neural Networks (ANN) and more recently Support Vector Machines (SVM), have been proposed for TSF. The accuracy of the best CI model is affected by both the selection of input...
Forecasting seasonal time series with computational intelligence : contribution...
Stepnicka, M.; Peralta Donate, Juan; Cortez, Paulo, 1971-; Vavricková, L.; Gutierrez Sanchez, GermanAccurate time series forecasting are important for displaying the manner in which the past contin- ues to affect the future and for planning our day to day activities. In recent years, a large litera- ture has evolved on the use of computational in- telligence in many forecasting applications. In this paper, several computational intelligence techniques (genetic algorithms, neural networks, support vec- tor mac...
Evolving sparsely connected neural networks for multi-step ahead forecasting
Peralta Donate, Juan; Cortez, Paulo, 1971-; Gutierrez Sanchez, German; Sanchis de Miguel, AraceliTime Series Forecasting (TSF) is an important tool to sup- port decision making. Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlin- ear learning and noise tolerance. However, the search for the best ANN is a complex task that highly affects the forecast- ing performance. In this paper, we propose a novel Sparsely connected Evolutionary ANN (SEANN), which evolves mor...
Evolving time-lagged feedforward neural networks for time series forecasting
Peralta Donate, Juan; Cortez, Paulo, 1971-; Gutierrez Sanchez, German; Sanchis de Miguel, AraceliTime Series Forecasting (TSF) is an important tool to sup- port both individual and organizational decisions. In this work, we propose a novel automatic Evolutionary Time- Lagged Feedforward Network (ETLFN) approach for TSF, based on an Estimation Distribution Algorithm (EDA) that evolves not only Artificial Neural Network (ANN) parame- ters but also which set of time lags are fed into the fore- casting model. ...
Weighted cross-validation evolving artificial neural networks to forecast time ...
Peralta Donate, Juan; Cortez, Paulo, 1971-; Gutierrez Sanchez, German; Sanchis de Miguel, AraceliAccurate 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...
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