How do ensemble methods improve time series forecasting accuracy?
Instruction: Discuss the concept of ensemble methods and explain how they can be used to enhance the accuracy of time series forecasts.
Updated
Example Answer
Combining forecasts can help when models make different useful errors, but it is not guaranteed to beat the best individual model. I would start with an equal-weight average of a few credible candidates, then compare it with each model on rolling forecast origins.
For demand, a seasonal baseline and a model with known promotion inputs might provide complementary information. I would learn any weights or stacking model from out-of-sample forecasts, keeping the final test period separate. For time-series bagging, the resampling must respect the relevant dependence rather than blindly shuffle observations. I would also examine bias and performance during important events. An ensemble adds maintenance cost and can combine the same failure several times, so I would retain it only when the evidence supports the tradeoff.
Make it your own
Name the models, why their errors may differ, and how you would fit combination weights.
Why this works
It makes improvement a testable hypothesis rather than a guarantee.
Follow-up to practice
What happens when every component misses the same event?