Previsões
ID
Idioma
Nome do modelo
Repositório
Descrição
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state CE.
Univariate neural prophet model
Forecast de novos casos para o geocode 2408102 entre 2022-01-01 e 2023-07-02
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state RO.
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 e das cidades clusterizadas com ele
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state TO.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state RJ.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state MA.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on GO_info_dengue_test_2
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on TO_info_dengue_test_2
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state PA.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SC_info_dengue_test_2
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on SP_info_dengue_test_1
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on RJ_info_dengue_test_1
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on PB_info_dengue_test_1
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on DF_info_dengue_test_1
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (SE)
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on AM_info_dengue_test_1
LSTM model for Infodengue Sprint
Predictions for 2023 in BA using the baseline architecture
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AL.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state AC.
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 1 in the state AM.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 1 in the state SE.
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 2507507
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.
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for validation test 2 in the state DF.
LSTM model for Infodengue Sprint
Predictions for 2024 in DF using the att_3 architecture
Model 2 - Weekly and yearly (rw1) 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.
Model 2 - Weekly and yearly (rw1) 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 follows a RW(1) process.
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.
700 previsões
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