Previsões
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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
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
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
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 is assumed to be iid.
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (RO)
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
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.
LSTM model for Infodengue Sprint
Predictions for 2024 in RS using the baseline architecture
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model
infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on RN_info_dengue_test_2
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.
LSTM model for Infodengue Sprint
Predictions for 2024 in RO using the comb_att_n architecture
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 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.
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (DF)
LSTM model for Infodengue Sprint
test for sprint 2025 preds of the arima model in MG
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2304400 como input
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
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 todos as cidades clusterizadas com 2211001 como input
LSTM model for Infodengue Sprint
Predictions for 2023 in MA using the baseline architecture
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 1 for three-way interaction model (AC)
Random Forest model with uncertainty computed with conformal prediction
Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2704302 como input
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2111300 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2111300
Temp-SPI Interaction Model
Validation test 1 for three-way interaction model (PB)
Deep learning model using BI-LSTM Layers
Forecast de novos casos para o geocode 2408102 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele
Model 2 - Weekly and yearly (rw1) components
This upload represents the epidemic prediction for validation test 2 in the state PI.
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.
700 previsões
https://api.mosqlimate.org/api/registry/predictions/?page=23&per_page=30&
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