Early Prediction of Dengue Cases Using Time Series Model

Dengue, is a seasonal infectious disease has been spreading widely throughout the world. Because of this many of the people are losing their lives. So public Health organizations and many other government organizations have been conducting surveys of dengue cases and they got to know that because of weather conditions like rain, precipitation, humidity are some of the main causes for dengue prediction. Also they use some ML algorithms and some Time Series models to predict dengue cases. Time series models they have used as (ARIMA) model and SARIMA model and seasonality. But out of this ARIMA model looks best to predict dengue cases. So we have taken ARIMA model to predicting the forecast of dengue cases. In ARIMA we have done two methods to find out the order of the model where it has values as autoregressive, moving average and non-seasonal in seasonality autocorrelation, partial autocorrelation, and AUTO-ARIMA for find out the best model for the order then we have checked for stationary also. If its stationary then we have proceeded to prediction of forecasting dengue cases, if it’s not then we have converted it into stationary and then we moved on to do ARIMA best fit which gives the forecasting cases.