Ricardo Leon Talavera-Llames

• Computer Engineer, Ph.D.
• Research Assistant at Pablo de Olavide University

This paper introduces a novel algorithm for big data time series forecasting. Its main novelty lies in its ability to deal with multivariate data, i.e. to consider multiple time series simultaneously, in order to make multi-output predictions. Real-world processes are typically characterised by several interrelated variables, and the future occurre...

This paper presents ensemble models for forecasting big data time series. An ensemble composed of three methods (decision tree, gradient boosted trees and random forest) is proposed due to the good results these methods have achieved in previous big data applications. The weights of the ensemble are computed by a weighted least square method. Two s...

A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm is introduced in this work. Such an algorithm has been developed for distributed computing under the Apache Spark framework. Every phase of the algorithm is explained in this work, along with how the optimal values of the input parameters required for the a...

In recent years the available volume of information has grown considerably due to the development of new technologies such as the sensor networks or smart meters, and therefore, new algorithms able to deal with big data are necessary. In this work the distributed version of the k-means algorithm in the Apache Spark framework is proposed in order to...

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and implement the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, e...