Subset Models for Multivariate Time Series Forecast

被引：0

作者：

Saldanha, Raphael ^{[1
]}

Ribeiro, Victor ^{[2
]}

Pena, Eduardo H. M. ^{[3
]}

Pedroso, Marcel ^{[4
]}

Akbarinia, Reza ^{[1
]}

Valduriez, Patrick ^{[1
,2
]}

Porto, Fabio ^{[2
]}

机构：

[1] Univ Montpellier, Inria, CNRS, LIRMM, Montpellier, France

[2] DEXL, LNCC, Petropolis, RJ, Brazil

[3] UTFPR, DACOM, Campo Mourao, Brazil

[4] Fundacao Oswaldo Cruz, ICICT, LIS, PCDaS, Rio De Janeiro, Brazil

来源：

2024 IEEE 40TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, ICDEW | 2024年

关键词：

domain diversity; subsets; machine learning; dengue; climate; BRAZIL;

D O I：

10.1109/ICDEW61823.2024.00016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：86 / 90

页数：5

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