@misc{10481/94396,
year = {2024},
month = {9},
url = {https://hdl.handle.net/10481/94396},
abstract = {Time series forecasting is undoubtedly a key area in machine learning due to the numerous
fields where it is crucial to estimate future data points of sequences based on a set of previously
observed values. Deep learning has been successfully applied to this area. On the other hand,
growing concerns about the steady increase in the amount of resources required by deep learningbased
tools have made Green AI gain traction as a move towards making machine learning more
sustainable. In this paper, we present a deep learning-based time series forecasting methodology
called GreeNNTSF, which aims to reduce the size of the resulting model, thereby diminishing the
associated computational and energetic costs without giving up adequate forecasting performance.
The methodology, based on the ODF2NNA algorithm, produces models that outperform state-of-theart
techniques not only in terms of prediction accuracy but also in terms of computational costs and
memory footprint. To prove this claim, after presenting the main state-of-the-art methods that utilize
deep learning for time series forecasting and introducing our methodology we test GreeNNTSF on a
selection of real-world forecasting problems that are commonly used as benchmarks, such as SARSCoV-2 and PhysioNet (medicine), Brazilian Weather (climate), WTI and Electricity (economics), and
Traffic (smart cities). The results of each experiment conducted objectively demonstrate, rigorously
following the experimentation presented in the original papers that addressed these problems, that
our method is more competitive than other state-of-the-art approaches, producing more accurate and
efficient models.},
organization = {Spanish Ministry of Economy, Industry,
and Competitiveness (PID2023-151336OB-I00) with the co-financing of the European Union (FEDER)},
organization = {Proyecto PID2020-118224RB-I00 financiando por Ministerio de Ciencia, Innovación y Universidades,
MICIN/AEI/10.13039/501100011033},
publisher = {MDPI},
keywords = {Green AI},
keywords = {Dense feed-forward neural network simplification},
keywords = {Time series forecasting},
title = {An Efficient Green AI Approach to Time Series Forecasting Based on Deep Learning},
doi = {10.3390/bdcc8090120},
author = {Balderas, Luis and Lastra Leidinger, Miguel and Benítez Sánchez, José Manuel},
}