  Time-Series Forecasting is a powerful data modeling discipline that analyzes
historical observations to predict future values of a time-series. It has been
utilized in numerous applications, including but not limited to economics,
meteorology, and health. In this paper, we use time-series forecasting
techniques to model and predict the future incidence of chickenpox. To achieve
this, we implement and simulate multiple models and data preprocessing
techniques on a Hungary-collected dataset. We demonstrate that the LSTM model
outperforms all other models in the vast majority of the experiments in terms
of county-level forecasting, whereas the SARIMAX model performs best at the
national level. We also demonstrate that the performance of the traditional
data preprocessing method is inferior to that of the data preprocessing method
that we have proposed.
