Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

被引：27

作者：

Fernandes, Filipe ^{[1
]}

Stefenon, Stefano Frizzo ^{[1
,2
,3
]}

Seman, Laio Oriel ^{[4
]}

Nied, Ademir ^{[1
]}

Silva Ferreira, Fernanda Cristina ^{[5
]}

Mazzetti Subtil, Maria Cristina ^{[5
]}

Rodrigues Klaar, Anne Carolina ^{[5
]}

Quietinho Leithardt, Valderi Reis ^{[6
,7
]}

机构：

[1] Santa Catarina State Univ, Elect Engn Grad Program, Rua Malschitzki 200, Joinville, Brazil

[2] Fdn Bruno Kessler, Ist Ric Sci & Tecnol, Via Sommar 18, I-38123 Povo, Trento, Italy

[3] Univ Udine, Comp Sci & Artificial Intelligence, Via Sci 206, I-33100 Udine, Italy

[4] Univ Vale Itajai, Grad Program Appl Comp Sci, Rua Uruguai 458, BR-88302202 Itajai, SC, Brazil

[5] Univ Planalto Catarinense, Av Mal Castelo Branco 170, Lages, SC, Brazil

[6] Inst Politecn Portalegre, Res Ctr Endogenous Resources Valorizat, VALORIZA, P-7300555 Portalegre, Portugal

[7] Univ Lusofona Humanidades & Tecnol, COPELABS, Campo Grande 376, P-1749024 Lisbon, Portugal

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2022年 / 42卷 / 06期

关键词：

Long short-term memory; COVID-19; spreading viruses; SUPPORT VECTOR REGRESSION; DECOMPOSITION; OUTBREAK; FORECAST; CHINA; SHIP;

D O I：

10.3233/JIFS-212788

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic's control capacity. The use of LSTM stacking shows an R-2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.

引用

页码：6221 / 6234

页数：14

共 64 条

[1] Optimization Method for Forecasting Confirmed Cases of COVID-19 in China [J].

Al-qaness, Mohammed A. A. ;

Ewees, Ahmed A. ;

Fan, Hong ;

Abd El Aziz, Mohamed .

JOURNAL OF CLINICAL MEDICINE, 2020, 9 (03)

[2]

BAO W, 2017, PLOS ONE, V12, DOI DOI 10.1371/journal.pone.0180944

[3]

Catarina, 2020, PLANTAO CORONAVIRUS

[4] Multi-information fusion neural networks for arrhythmia automatic detection [J].

Chen, Aiyun ;

Wang, Fei ;

Liu, Wenhan ;

Chang, Sheng ;

Wang, Hao ;

He, Jin ;

Huang, Qijun .

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 193

[5] Time series forecasting of COVID-19 transmission in Canada using LSTM networks [J].

Chimmula, Vinay Kumar Reddy ;

Zhang, Lei .

CHAOS SOLITONS & FRACTALS, 2020, 135

[6] Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning [J].

Dal Molin Ribeiro, Matheus Henrique ;

Stefenon, Stefano Frizzo ;

de Lima, Jose Donizetti ;

Nied, Ademir ;

Mariani, Viviana Cocco ;

Coelho, Leandro dos Santos .

ENERGIES, 2020, 13 (19)

[7] Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil [J].

Dal Molin Ribeiro, Matheus Henrique ;

da Silva, Ramon Gomes ;

Mariani, Viviana Cocco ;

Coelho, Leandro dos Santos .

CHAOS SOLITONS & FRACTALS, 2020, 135

[8] Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series [J].

Dal Molin Ribeiro, Matheus Henrique ;

Coelho, Leandro dos Santos .

APPLIED SOFT COMPUTING, 2020, 86

[9] Static Attitude Determination Using Convolutional Neural Networks [J].

dos Santos, Guilherme Henrique ;

Seman, Laio Oriel ;

Bezerra, Eduardo Augusto ;

Quietinho Leithardt, Valderi Reis ;

Mendes, Andre Sales ;

Stefenon, Stefano Frizzo .

SENSORS, 2021, 21 (19)

[10] Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram [J].

Erdenebayar, Urtnasan ;

Kim, Yoon Ji ;

Park, Jong-Uk ;

Joo, Eun Yeon ;

Lee, Kyoung-Joung .

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 180

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