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> and even a linear model with a few parameters Using a simple average of past performance to predict future performance is also a technique that is often dist
by roenxi 2y ago
> and even a linear model with a few parameters
Using a simple average of past performance to predict future performance is also a technique that is often disturbingly effective vs. standard practice. I suppose technically that is a linear model, but really deserves its own class.
- AstralStorm 2y agoUp to a point where the prediction runs afoul of the time horizon and changing unmodelled circumstances. They do not have sufficient explicit risk or variance management. Makes them highly fragile. There are more robust versions of the estimators... Still have a problem. Remember 2008? That market ran on these easy models.
- gwern 2y agoYes, 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 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/S0169207021001874 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/S0169207024001079 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.)