Towards Automatic Forecasting: Evaluation of Time-Series Forecasting Models for Chickenpox Cases Estimation in Hungary

TL;DR

This paper evaluates various time-series forecasting models, including LSTM and SARIMAX, for predicting chickenpox cases in Hungary, highlighting model performance differences at county and national levels.

Contribution

It introduces a novel data preprocessing method and compares multiple models, demonstrating their effectiveness in chickenpox case prediction in Hungary.

Findings

LSTM outperforms other models at county level

SARIMAX performs best at national level

Proposed preprocessing method improves forecast accuracy

Abstract

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.