Yes, exponential smoothing in forecasting is another classic example of the robustness of simple models. You can throw all your fancy ARIMAs and Box-Cox transforms at a time-series, and much of the time, it is hard to distinguish from a simple moving average.

Specifically, the Makridakis M forecasting competitions (https://en.wikipedia.org/wiki/Makridakis_Competitions) have shown for a long time that beating the baselines is shockingly difficult.

In fact, classic machine learning only really started to convincingly win with the second-to-last, M5: https://www.sciencedirect.com/science/article/pii/S016920702... ; and neural methods only just sort of began working with the latest one, M6: https://www.sciencedirect.com/science/article/pii/S016920702... . (Possibly with M7 we'll see scaled-up meta-learning Transformers finally start beating the Bayesian or decision-tree forecasters. But I don't know if or when a M7 might be held.)