Conference Papers Year : 2024

Subset Models for Multivariate Time Series Forecast

Abstract

Multivariate time series find extensive applications in conjunction with machine learning methodologies for scenario forecasting across various domains. Nevertheless, certain domains exhibit inherent complexities and diversities, which detrimentally impact the predictive efficacy of global models. This ongoing study introduces a Subset Modeling Framework designed to acknowledge the inherent diversity within a domain's multivariate space. Comparative assessments between subset models and global models are conducted in terms of performance, revealing compelling findings and suggesting the potential for further exploration and refinement of this novel framework.
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Dates and versions

lirmm-04711300 , version 1 (26-09-2024)

Identifiers

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Raphael de Freitas Saldanha, Victor Ribeiro, Eduardo Peña, Marcel Pedroso, Reza Akbarinia, et al.. Subset Models for Multivariate Time Series Forecast. ICDEW 2024 - IEEE 40th International Conference on Data Engineering Workshops, IEEE, May 2024, Utrecht, Netherlands. pp.86-90, ⟨10.1109/ICDEW61823.2024.00016⟩. ⟨lirmm-04711300⟩
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