poster

Evolutionary Feature Selection for Time-Series Forecasting

Authors:

Maria Lourdes Linares-Barrera,

Manuel J. Jimenez-Navarro,

Isabel Sofia Brito,

José C. Riquelme,

María Martínez-BallesterosAuthors Info & Claims

SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

Pages 395 - 399

https://doi.org/10.1145/3605098.3636191

Published: 21 May 2024 Publication History

Abstract

In machine learning, feature selection is crucial for pinpointing the key subset of features that enhances interpretability and preserves or boosts the model's original performance. Filter methods, which assess features using statistical metrics, are particularly notable. Recently, a novel metric called Conditional Dependence Coefficient has been proposed to measure the dependence between subsets of variables.

This paper introduces a novel filter feature selection method that integrates the Conditional Dependence Coefficient metric with an evolutionary algorithm to find the optimal feature subset. This approach combines the adaptability of genetic algorithms with the strength of an intuitive metric. Unlike many filter-based methods, our technique does not rely on parameters directly linked to the number of features (like thresholds). Moreover, it evaluates the collective merit of feature subsets rather than individual significance.

We conducted tests on six different multivariate time-series datasets to address the forecasting challenge, determining the relevant lags. Considering no selection as baseline, experimental results indicate that our approach is competitive in terms of efficacy while demonstrating a reduction in the number of features selected.

References

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Digital Library

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J. Wu, X. Chen, H. Zhang, L. Xiong, H. Lei, and S. Deng. 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology 17, 1 (2019), 26--40.

Cited By

Jiménez-Navarro MLovrić MKecorius SNyarko EMartínez-Ballesteros M(2024)Explainable deep learning on multi-target time series forecasting: an air pollution use caseResults in Engineering10.1016/j.rineng.2024.103290(103290)Online publication date: Nov-2024
https://doi.org/10.1016/j.rineng.2024.103290

Index Terms

Evolutionary Feature Selection for Time-Series Forecasting
1. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Conferences

SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

April 2024

1898 pages

ISBN:9798400702433

DOI:10.1145/3605098

Chair:
Jiman Hong,
Program Chairs:
Juw Won Park,
Adam Przybyłek

Copyright © 2024 Copyright held by the owner/author(s).

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2024

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Ministerio de Ciencia e Innovación

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SAC '24

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SIGAPP

SAC '24: 39th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2024

Avila, Spain

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Jiménez-Navarro MLovrić MKecorius SNyarko EMartínez-Ballesteros M(2024)Explainable deep learning on multi-target time series forecasting: an air pollution use caseResults in Engineering10.1016/j.rineng.2024.103290(103290)Online publication date: Nov-2024
https://doi.org/10.1016/j.rineng.2024.103290

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