@misc{10481/94661,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10481/94661},
abstract = {In this study, the energy efficiency of time series forecasting algorithms is addressed in
a broad context, highlighting the importance of optimizing energy consumption in computational
applications. The purpose of this study is to compare the energy efficiency and accuracy of algorithms
implemented in different frameworks, specifically Darts, TensorFlow, and Prophet, using the ARIMA
technique. The experiments were conducted on a local infrastructure. The Python library CodeCarbon
and the physical energy consumption measurement device openZmeter were used to measure the
energy consumption. The results show significant differences in energy consumption and algorithm
accuracy depending on the framework and execution environment. We conclude that it is possible to
achieve an optimal balance between energy efficiency and accuracy in time series forecasting, which
has important implications for developing more sustainable and efficient applications. This study
provides valuable guidance for researchers and professionals interested in the energy efficiency of
forecasting algorithms.},
organization = {Ministerio Español de Ciencia e Innovación under
project numbers PID2023-147409NB-C21, PID2020-115570GB-C22 and PID2022-137461NB-C32
funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, as well as TIC251-G-FEDER and
C-ING-027-UGR23 projects, funded by ERDF/EU},
publisher = {MDPI},
keywords = {Time series},
keywords = {Forecasting},
keywords = {Green computing},
title = {Energy Efficiency Evaluation of Frameworks for Algorithms in Time Series Forecasting},
doi = {10.3390/engproc2024068030},
author = {Aquino Brítez, Sergio and García Sánchez, Pablo and Ortiz, Andrés and Aquino Brítez, Diego},
}