The article compares different methods for predicting future trends in data. It finds that using a specific method called iterated forecasting works best when trying to minimize errors in small samples or for long-term predictions. On the other hand, direct forecasting is better when the model used to make predictions is not quite right. By looking at a large set of variables, the researchers show that using multiple pieces of information together can improve predictions, especially for short-term forecasts.