Evolving time-lagged feedforward neural networks for time series forecasting

Time Series Forecasting (TSF) is an important tool to support 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) parameters but also which set of time lags are fed into the forecasting model. Such approach is compared with similar strategy that only selects ANN parameter and the conventional TSF ARIMA methodology. Several experiments were held by considering six time series from distinct domains. The obtained multi-step ahead forecasts were evaluated using SMAPE error criteria. Overall, the proposed ETLFN method obtained the best forecasting results. Moreover, it favors simpler neural network models, thus requiring less computational effort.