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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...
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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