Boosting Algorithm (BA) is state-of-the-art in major competitions, especially in the M4 and M5 time series forecasting competitions. However, the use of BA requires tedious feature engineering work with blindness and randomness, which results in a serious waste of time. In this work, we try to guide the initial feature engineering operations in virtue of the explanation results of the SHAP technique, and meanwhile, the traditional Feature Importance (FI) method is also taken into account. Previous BA explanation works have rarely focused on forecasting, so the contribution of this work is (1) to develop a BA explanation framework-“FI-SHAP”, which focuses on time series forecasting, (2) to improve the efficiency of feature engineering. At the same time, to measure explainability performance, (3) we also establish a new practical evaluation framework that attempts to remove development barriers in the field of explainable AI.