Previsões


ID

Idioma

Nome do modelo

Autor

Repositório

Data da previsão Tipo ID do Modelo

Descrição

484

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2024 in SP using the baseline architecture

228

LSTM model for Infodengue Sprint

2024-08-20 Model 21

Predictions for 2023 in MG using the baseline architecture

463

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in RJ using the baseline architecture

124

Temp-SPI Interaction Model

2024-08-14 Model 22

Validation test 1 for three-way interaction model (SC)

199

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.

415

BB-M

2024-09-02 Model 30

Prediction for AP in 2023 (train 1)

478

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in RN using the baseline architecture

471

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in PA using the comb_att_n architecture

454

BB-M

2024-09-02 Model 30

Prediction for SE in 2024 (train 2)

431

BB-M

2024-09-02 Model 30

Prediction for SC in 2023 (train 1)

416

BB-M

2024-09-02 Model 30

Prediction for BA in 2023 (train 1)

453

BB-M

2024-09-02 Model 30

Prediction for SC in 2024 (train 2)

442

BB-M

2024-09-02 Model 30

Prediction for MS in 2024 (train 2)

25

Random Forest model with uncertainty computed with conformal prediction

2023-09-14 Model 5

Forecast de novos casos para o geocode 2304400 entre 2022-01-01 e 2023-01-01 usando apenas os dados de 2304400 como input

477

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in SE using the baseline architecture

12

Random Forest model with uncertainty computed with conformal prediction

2023-09-14 Model 5

Forecast de novos casos para o geocode 2111300 entre 2022-01-01 e 2023-01-01 usando apenas os dados de todos as cidades clusterizadas com 2111300 como input

216

BB-M

2024-08-19 Model 30

Prediction for CE in 2024

210

BB-M

2024-08-19 Model 30

Prediction for AM in 2023

109

Temp-SPI Interaction Model

2024-08-14 Model 22

Validation test 1 for three-way interaction model (GO)

430

BB-M

2024-09-02 Model 30

Prediction for RS in 2023 (train 1)

414

BB-M

2024-09-02 Model 30

Prediction for AL in 2023 (train 1)

470

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in RO using the comb_att_n architecture

429

BB-M

2024-09-02 Model 30

Prediction for RR in 2023 (train 1)

462

LSTM model for Infodengue Sprint

2024-09-02 Model 21

Predictions for 2023 in SP using the baseline architecture

441

BB-M

2024-09-02 Model 30

Prediction for MA in 2024 (train 2)

130

Temp-SPI Interaction Model

2024-08-14 Model 22

Validation test 2 for three-way interaction model (AM)

19

Random Forest model with uncertainty computed with conformal prediction

2023-09-14 Model 5

Forecast de novos casos para o geocode 2704302 entre 2022-01-01 e 2023-01-01 usando apenas os dados de 2704302 como input

33

Deep learning model using BI-LSTM Layers

2023-09-12 Model 6

Forecast de novos casos para o geocode 2408102 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode 2408102

40

Deep learning model using BI-LSTM Layers

2023-09-12 Model 6

Forecast de novos casos para o geocode 2211001 entre 2022-01-01 e 2023-01-01 usando apenas os dados do geocode e das cidades clusterizadas com ele

440

BB-M

2024-09-02 Model 30

Prediction for ES in 2024 (train 2)

700 previsões

https://api.mosqlimate.org/api/registry/predictions/?page=16&per_page=30&


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