Auto-regressive Multi-variable Auto-encoder - Oliveira, Emerson V.; Santos, David H. do; Goncalves, Luiz M. G. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2022: Anais Estendidos da XXXV Conference on Graphics, Patterns and Images, 2022)
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.