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Highlights: [•] We present a method for optimization of the activation functions for ANN. [•] The proposed optimization method uses Simulated Annealing and Tabu Search. [•] The proposed method is good for forecasting distinct time series. [ABSTRACT FROM AUTHOR]

We present here an original work that applies meta-learning approaches to select models for time-series forecasting. In our work, we investigated two meta-learning approaches, each one used in a different case study. Initially, we used a single machine learning algorithm to select among two models to forecast stationary time series (case study I). Following, we used the NOEMON approach, a more recent work in the meta-learning area, to rank three models used to forecast time series of the M3-Competition (case study II). The experiments performed in both case studies revealed encouraging results. [Copyright &y& Elsevier]