论文标题
迈向自动预测：匈牙利的水痘病例估算的时间序列预测模型
Towards Automatic Forecasting: Evaluation of Time-Series Forecasting Models for Chickenpox Cases Estimation in Hungary
论文作者
论文摘要
时间序列预测是一项强大的数据建模学科，可以分析历史观察以预测时间序列的未来价值。它已用于许多应用程序，包括但不限于经济学，气象和健康。在本文中，我们使用时间序列预测技术来建模和预测水痘的未来发生率。为了实现这一目标，我们在匈牙利收集的数据集上实现并模拟了多个模型和数据预处理技术。我们证明，在县级预测方面，LSTM模型在绝大多数实验中的所有其他模型都优于所有其他模型，而Sarimax模型在国家一级表现最佳。我们还证明，传统数据预处理方法的性能不如我们提出的数据预处理方法的性能。
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.
