Abstract or Introduction
Analysis and modelling of the daily observations is of the interest for both academic and practical needs during the worst public health crisis in decades. In this paper we propose a Boosting-based Quantile Autoregressive Tree (BQART) model to estimate the evolution in reported cases and fatality of the COVID-19 pandemic. The proposed approach benefit from the boosting methodology and the additive quantile regression to overcome challenges of unknown probabilistic distribution in the autoregressive variable and location shift in the observed data. The simple additive structure and binary autoregressive tree representation further improve the interpretability of the model and help to clearly illustrate the results.
The estimated results for the USA and Singapore were discussed in details with more results for other countries in the appendix. While the shape and structure of estimated trees represent the autoregressive properties observed in the data, the model output helps to demonstrate improved accuracy in time series forecasting and analysis. These results should encourage the use of machine learning based tree ensembles in time-series modelling where model performance and interpretability is sought.
- Quote paper
- Yang Liu (Author), 2020, A Boosting-based Quantile Autoregressive Tree Model for the COVID-19 Time Series, Munich, GRIN Verlag, https://www.grin.com/document/923166