Multivariate Prediction of Energy Time Series by Autoencoded LSTM Networks

In this paper, a novel approach for the multivariate prediction of energy time series is presented. It is based on the Long Short-Term Memory deep neural network. The latter is made up of two stacked recurrent layers and it is used in two different training configurations. First, an encoder-decoder structure is implemented in order to extract meaningful representative features from the time series. Then, this embedded data are used to improve the actual prediction. To prove the goodness of our approach, its performance is compared with two different benchmarks. The numerical results show that the proposed model outperforms the aforementioned benchmarks.