Previsões


ID

Idioma

Nome do modelo

Autor

Repositório

Data da previsão Tipo ID do Modelo

Descrição

257

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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.

286

BB-M

2024-08-26 Model 30

Prediction for MS in 2025

285

BB-M

2024-08-26 Model 30

Prediction for MG in 2025

359

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

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.

283

BB-M

2024-08-26 Model 30

Prediction for GO in 2025

309

Temp-SPI Interaction Model

2024-08-28 Model 22

2024/25 forecast for three-way interaction model (BA)

282

BB-M

2024-08-26 Model 30

Prediction for ES in 2025

312

Temp-SPI Interaction Model

2024-08-28 Model 22

2024/25 forecast for three-way interaction model (SP)

788

LSTM model for Infodengue Sprint

2024-09-12 Model 21

Predictions for 2024 in PR using the baseline architecture

782

LSTM model for Infodengue Sprint

2024-09-12 Model 21

Predictions for 2024 in PE using the baseline architecture

375

Model 2 - Weekly and yearly (rw1) components

2024-08-29 Model 28

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PR.

752

Model 2 - Weekly and yearly (rw1) components

2024-09-11 Model 28

This upload represents the epidemic prediction for validation test 2 in the state GO.

333

LSTM model for Infodengue Sprint

2024-08-28 Model 21

Predictions for 2025 in PB using the baseline architecture

280

BB-M

2024-08-26 Model 30

Prediction for CE in 2025

304

Temp-SPI Interaction Model

2024-08-28 Model 22

2024/25 forecast for three-way interaction model (PB)

563

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

2024-09-09 Model 34

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on GO_info_dengue_test_2

519

Model 1 - Weekly and yearly (iid) components

2024-09-02 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PB.

596

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

2024-09-09 Model 34

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on DF_info_dengue_2024_2025

567

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model

2024-09-09 Model 34

infodengue_sprint_24_25_hybrid_CNN_LSTM_ensemble_model Prediction on CE_info_dengue_2024_2025

539

Model 2 - Weekly and yearly (rw1) components

2024-09-02 Model 28

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state RO.

259

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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.

269

Model 2 - Weekly and yearly (rw1) components

2024-08-15 Model 28

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.

203

Model 2 - Weekly and yearly (rw1) components

2024-08-15 Model 28

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.

353

LSTM model for Infodengue Sprint

2024-08-28 Model 21

Predictions for 2025 in RO using the comb_att_n architecture

371

Model 2 - Weekly and yearly (rw1) components

2024-08-29 Model 28

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state AM.

267

Model 2 - Weekly and yearly (rw1) components

2024-08-15 Model 28

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.

301

BB-M

2024-08-26 Model 30

Prediction for TO in 2025

504

Model 1 - Weekly and yearly (iid) components

2024-09-02 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PA.

195

Model 1 - Weekly and yearly (iid) components

2024-08-15 Model 27

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.

360

Model 1 - Weekly and yearly (iid) components

2024-08-29 Model 27

This upload represents the epidemic prediction for unseen data from the period of 2024-06-16 to 2025-10-05 in the state PR. 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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