A blockwise embedding for multi-day-ahead prediction of energy time series by randomized deep neural networks

Nowadays, deep learning is gaining attraction as one of the most successful paradigm for a plethora of machine learning applications. While its benefits are undoubted, the high computational burden associated with its training algorithms and cross-validation procedures is stimulating new lines of research. To this end, randomized deep neural networks are one of the best alternatives in terms of efficiency-to-accuracy balance. In this paper, we present a deep neural architecture that uses randomization of some parameters in a complex structure whose novelty is twofold: it embeds past samples of the time series by using daily blocks in the input frame of a convolutional layer; it predicts a day on the whole by solving a suitable regression problem. The proposed randomized approach is compared with state-of-the-art prediction algorithms on the challenging context related to energy time series, where the day-ahead prediction is usual, obtaining comparable or even better results in terms of forecasting accuracy and training time.