Rubén Pérez-Chacón

TL;DR: A methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments is proposed.

TL;DR: In this paper, a bio-inspired metaheuristic was proposed to simulate how the coronavirus spreads and infects healthy people, where relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate were introduced into the model to simulate the virus activity as accurately as possible.

TL;DR: In this paper, a bioinspired metaheuristic is proposed to simulate how the coronavirus spreads and infects healthy people, where relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the Coronavirus activity.

TL;DR: A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm for distributed computing under the Apache Spark framework is introduced, leading to the conclusion that the proposed algorithm is highly suitable for bigData environments.

TL;DR: This work proposes a novel algorithm to forecast big data time series, based on the well-established Pattern Sequence-based Forecasting algorithm, which uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust.