Previsões
ID
Idioma
Nome do modelo
Repositório
Descrição
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 3106200 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 3106200 como input
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 3106200 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2611606 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (AC)
Model 1 - Weekly and yearly (iid) components
The model is founded on a structural decomposition designed for modeling counting series, employing a Poisson distribution. The log intensity is defined by the sum of weekly and yearly components, where the first one is defined as a AR(1) process and the last one is assumed to be iid.
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2927408 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (MA)
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (CE)
Model 1 - Weekly and yearly (iid) components
The model is founded on a structural decomposition designed for modeling counting series, employing a Poisson distribution. The log intensity is defined by the sum of weekly and yearly components, where the first one is defined as a AR(1) process and the last one is assumed to be iid.
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2211001 entre 2022-01-01 e 2023-01-01 usando apenas os dados de 2211001 como input
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2507507 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2304400
LSTM model for Infodengue Sprint
Predictions for 2024 in MA using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2024 in RR using the comb_att_n architecture
LSTM model for Infodengue Sprint
Predictions for 2024 in BA using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2024 in SC using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2023 in RR using the comb_att_n architecture
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (AL)
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2800308 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2800308
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2611606 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2611606
Model 1 - Weekly and yearly (iid) components
The model is founded on a structural decomposition designed for modeling counting series, employing a Poisson distribution. The log intensity is defined by the sum of weekly and yearly components, where the first one is defined as a AR(1) process and the last one is assumed to be iid.
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (RR)
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2704302
LSTM model for Infodengue Sprint
Predictions for 2023 in PB using the baseline architecture
LSTM model for Infodengue Sprint
Predictions for 2023 in AC using the comb_att_n architecture
700 previsões
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