Ensemble learning methods are used to improve performance by taking the cumulative results from multiple models to make a prediction. Ensemble models overcome the problem of overfitting by considering outputs of multiple models. This helps in overlooking modeling errors from any one model.

Ensemble learning can be a problem for time series models because every data point has a time dependency. However, if we choose to look at the data as a whole, we can overlook time dependency components. Time dependency components are conventional ensemble methods like bagging, boosting, random forests, and so on.