Dengue Trend Prediction by Learning Long Term Sequences

摘要

This paper formulates memory based learning long short-term sequences to forecast the dengue trend. We believe that this is the first work that shows how to use Long Short Term Memory Network to predict/forecast and determine the dengue trend. This problem of dengue trend prediction can be formulated as a temporal time series regression problem. Long short-term memory networks (LSTM) are the special kind of neural networks solve vanishing and exploding gradient problem that traditional neural networks suffered. LSTM networks have the ability to learn the temporal relationship of time series data and can handle the long-term dependency problems. We explore different LSTM regression methodologies and compare and report their accuracies to confirm the efficacy of our proposed method.