In this second chapter, we took on quite a complex time series competition, hence the easiest top solution we tried, it is actually fairly complex, and it requires coding quite a lot of processing functions. After you went through the chapter, you should have a better idea of how to process time series and have them predicted using gradient boosting. Favoring gradient boosting solutions over traditional methods, when you have enough data, as with this problem, should help you create strong solutions for complex problems with hierarchical correlations, intermittent series and availability of covariates such as events or prices or market conditions. In the following chapters, you will tackle with even more complex Kaggle competitions, dealing with images and texts. You will be amazed at how much you can learn by recreating top-scoring solutions and understanding their inner workings.