Predictions
ID
Lang
Model name
Repository
Description
273
Prophet model with PCA and vaiance threshold
predict for 2025 of the Prophet model in GO
347
LSTM model for Infodengue Sprint
Predictions for 2025 in MT using the att_3 architecture
322
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (PR)
367
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state CE.
319
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (TO)
227
LSTM model for Infodengue Sprint
Predictions for 2024 in PR using the baseline architecture
204
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.
343
LSTM model for Infodengue Sprint
Predictions for 2025 in RJ using the baseline architecture
145
Temp-SPI Interaction Model
Validation test 2 for three-way interaction model (PR)
223
LSTM model for Infodengue Sprint
Predictions for 2024 in GO using the att_3 architecture
312
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (SP)
359
Model 1 - Weekly and yearly (iid) components
This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state MG. 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.
258
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.
194
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.
335
LSTM model for Infodengue Sprint
Predictions for 2025 in PI using the baseline architecture
309
Temp-SPI Interaction Model
2024/25 forecast for three-way interaction model (BA)
193
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.
703 predictions
https://api.mosqlimate.org/api/registry/predictions/?page=8&per_page=30&
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