Spatio-temporal forecasting using wavelet transform-based decision trees with application to air quality and covid-19 forecasting
Description
We develop a new method that combines a decision tree with a wavelet transform to forecast time series data with spatial spillover effects. The method can not only improve prediction but also give good interpretability of the time series mechanism
