Resumen
It is well-known that part of the neural networks capacity is determined by their topology and the employed training process. How a neural network should be designed and how it should be updated every time that new data is acquired, is an issue that remains open since it its usually limited to a process of trial and error, based mainly on the experience of the designer. To address this issue, an algorithm that provides plasticity to recurrent neural networks (RNN) applied to time series forecasting is proposed. A decision-making grow and prune paradigm is created, based on the calculation of the data?s order, indicating in which situations during the re-training process (when new data is received), should the network increase or decrease its connections, giving as a result a dynamic architecture that can facilitate the design and implementation of the network, as well as improve its behavior. The proposed algorithm was tested with some time series of the M4 forecasting competition, using Long-Short Term Memory (LSTM) models. Better results were obtained for most of the tests, with new models both larger and smaller than their static versions, showing an average improvement of up to 18%.