Ricerc@Sapienza

A novel deep learning approach in proposed in this paper for multivariate prediction of energy time series. It is developed by using Convolutional Neural Network and Long Short-Term Memory models, in such a way that several correlated time series can be joined and filtered together considering the long term dependencies on the whole information. The learning scheme can be viewed as a stacked deep neural network where one or more layers are superposed, feeding their output in the sequent layer's input.

We propose a deep learning approach for multivariate forecasting of energy time series. It is developed by using Long Short-Term Memory deep neural networks so that different related time series, incorporating information of longterm dependencies, can be joined together as a multidimensional input of the deep neural network. The learning scheme can be represented as a stacked LSTM network in which one or more layers are cascaded, feeding their output to the input of the sequent layer.