Deep Learning-Based Approach for Time Series Forecasting with Application to Electricity Load

This paper presents a novel method to predict times series using deep learning. In particular, the method can be used for arbitrary time horizons, dividing each predicted sample into a single problem. This fact allows easy parallelization and adaptation to the big data context. Deep learning implementation in H2O library is used for each subproblem. However, H2O does not permit multi-step regression, therefore the solution proposed consists in splitting into h forecasting subproblems, being h the number of samples to be predicted, and, each of one has been separately studied, getting the best prediction model for each subproblem. Additionally, Apache Spark is used to load in memory large datasets and speed up the execution time. This methodology has been tested on a real-world dataset composed of electricity consumption in Spain, with a ten minute frequency sampling rate, from 2007 to 2016. Reported results exhibit errors less than 2%.