A machine learning approach to dengue forecasting: comparing LSTM, Random Forest and Lasso

• Ano de defesa: 2018
• Autor(a) principal: Mussumeci, Elisa
• Orientador(a): Coelho, Flávio Codeço
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Palavras-chave em Português: Aprendizado por máquina; Redes neurais
• Palavras-chave em Inglês: Machine learning; Neural networks; Time series; Forecasting; Epidemiology
• Link de acesso: http://hdl.handle.net/10438/24093

We used the Infodengue database of incidence and weather time-series, to train predictive models for the weekly number of cases of dengue in 790 cities of Brazil. To overcome a limitation in the length of time-series available to train the model, we proposed using the time series of epidemiologically similar cities as predictors for the incidence of each city. As Machine Learning-based forecasting models have been used in recent years with reasonable success, in this work we compare three machine learning models: Random Forest, lasso and Long-short term memory neural network in their forecasting performance for all cities monitored by the Infodengue Project.