RT Journal Article
T1 Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand
A1 Pérez Chacón, Rubén
A1 Asencio Cortés, Gualberto
A1 Martínez-Álvarez, Francisco
A1 Troncoso, Alicia
K1 Big data
K1 Time series
K1 Forecasting
K1 Electricity
AB This work proposes a novel algorithm to forecast big data time series. Based on the well-established Pattern Sequence-based Forecasting algorithm, this new approach has two major contributions to the literature. First, the improvement of the original algorithm with respect to the accuracy of predictions, and second, its transformation into the big data context, having reached meaningful results in terms of scalability. The algorithm uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust. Physical and cloud clusters have been used to carry out the experimentation, which consisted in applying the algorithm to real-world data from Uruguay electricity demand.
PB Elsevier
YR 2020
FD 2020-07-07
LK https://hdl.handle.net/10433/19787
UL https://hdl.handle.net/10433/19787
LA en
NO Information Sciences, vol 540, p. 160–174
NO Data Science and Big Data Research Lab
DS RIO
RD May 6, 2026