Auto-regressive Multi-variable Auto-encoder
Resumo
Due to the global pandemic disclaimer caused by the SARS-COV-2 virus propagation, also called COVID-19, governments, institutions, and researchers have mobilized intending to try to mitigate the effects caused by the virus on society. Some approaches were proposed and applied to try to make predictions of the behavior of possible pandemics indicators. Among those methodologies, some models are data orientated, also known as data-driven, which had considerable prominence over the others. Artificial Neural Networks are a widely used model among datadriven models. In this work, we propose a novel Auto-Encoder RNA architecture. This architecture aims to forecast time series related to the COVID-19 pandemic, particularly the number of deaths. The model uses as inputs possible associated time series with the desired forecasting. In the experiments, we used the representation in time series from the number of COVID-19 cases, deaths, temperature, humidity, and the Air Quality Index (AQI) of São Paulo city in Brazil. The results show that the model has a prominent forecasting accuracy for the COVID-19 deaths time series.
Palavras-chave:
machine learning, artificial neural network, auto-encoder, pandemic, COVID-19
Referências
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L. Djaparidze and F. A. Lois, "Sars-cov-2 waves in europe: A 2-stratum seirs model solution," medRxiv, 2020.
A. M. Elsaid and M. S. Ahmed, "Indoor air quality strategies for air-conditioning and ventilation systems with the spread of the global coronavirus (covid-19) epidemic: Improvements and recommendations," Environmental Research, p. 111314, 2021.
H. Xu, C. Yan, Q. Fu, K. Xiao, Y. Yu, D. Han, W. Wang, and J. Cheng, "Possible environmental effects on the spread of covid-19 in china," Science of the Total Environment, vol. 731, p. 139211, 2020.
I. M. Ismail, M. I. Rashid, N. Ali, B. A. S. Altaf, and M. Munir, "Temperature, humidity and outdoor air quality indicators influence covid-19 spread rate and mortality in major cities of saudi arabia," Environmental Research, p. 112071, 2021.
E. D. Freitas, S. A. Ibarra-Espinosa, M. E. Gavidia-Calderón, A. Rehbein, S. A. Abou Rafee, J. A. Martins, L. D. Martins, U. P. Santos, M. F. Ning, M. F. Andrade et al., "Mobility restrictions and air quality under covid-19 pandemic in são paulo, brazil," 2020.
B. Coppin, Artificial Intelligence Illuminated, ser. Jones and Bartlett illuminated series. Jones and Bartlett Publishers, 2004. [Online]. Available: https://books.google.com.br/books?id=LcOLqodW28EC
J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, "Deep learning for time series forecasting: A survey," Big Data, vol. 9, no. 1, pp. 3-21, 2021.
R. H. Shumway, D. S. Stoffer, and D. S. Stoffer, Time series analysis and its applications. Springer, 2000, vol. 3.
Y. Lecun, "Phd thesis: Modeles connexionnistes de l'apprentissage (connectionist learning models)," 1987.
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning. MIT press Cambridge, 2016, vol. 1.
J. Zhai, S. Zhang, J. Chen, and Q. He, "Autoencoder and its various variants," in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018, pp. 415-419.
W. Bao, J. Yue, and Y. Rao, "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PloS one, vol. 12, no. 7, p. e0180944, 2017.
A. Sagheer and M. Kotb, "Unsupervised pre-training of a deep lstm-based stacked autoencoder for multivariate time series forecasting problems," Scientific reports, vol. 9, no. 1, pp. 1-16, 2019.
——, "Time series forecasting of petroleum production using deep lstm recurrent networks," Neurocomputing, vol. 323, pp. 203-213, 2019.
M. R. Ibrahim, J. Haworth, A. Lipani, N. Aslam, T. Cheng, and N. Christie, "Variational-lstm autoencoder to forecast the spread of coronavirus across the globe," PloS one, vol. 16, no. 1, p. e0246120, 2021.
D. P. Aragão, E. V. Oliveira, A. A. Bezerra, D. H. dos Santos, A. G. da Silva Junior, I. G. Pereira, P. Piscitelli, A. Miani, C. Distante, J. S. Cuno, A. Conci, and L. M. Gonçalves, "Multivariate data driven prediction of covid-19 dynamics: Towards new results with temperature, humidity and air quality data," Environmental Research, vol. 204, p. 112348, 2022. [Online]. Available: [link]
I. G. Pereira, J. M. Guerin, A. G. Silva Júnior, G. S. Garcia, P. Piscitelli, A. Miani, C. Distante, and L. M. G. Gonçalves, "Forecasting covid-19 dynamics in brazil: a data driven approach," International Journal of Environmental Research and Public Health, vol. 17, no. 14, p. 5115, 2020.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "Pytorch: An imperative style, high-performance deep learning library," in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 8024- 8035. [Online]. Available: [link].
World health organization - coronavirus disease (covid-19) pandemic. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019
World air quality index project. [Online]. Available: [link]
Publicado
24/10/2022
Como Citar
OLIVEIRA, Emerson V.; SANTOS, David H. do; GONCALVES, Luiz M. G..
Auto-regressive Multi-variable Auto-encoder. In: WORKSHOP DE ANÁLISE DE PADRÕES E DADOS DA DINÂMICA DE PANDEMIAS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 147-152.
DOI: https://doi.org/10.5753/sibgrapi.est.2022.23279.
