Black-box algorithms with outstanding performance have been widely used in various fields; however, the lack of interpretability leads to great difficulties in troubleshooting and model improvement, hence severely confining the practical application. In response, we propose a framework that...

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve...