Do Different Models Pick Up Different Components in a Time Series?

Different models pick up different components in a time series and time series, like Univariate Data is very complex. Different models will pick up different squiggles better than others. So when you overlay all of them onto the same plot, they start canceling each other out. And what you're starting to do is generalize better to the overall trend. The end goal of a well trained supervised learning model is generalization. We don't want 100% accuracy. But an important thing to bring up with that is be careful how far out in the future you're trying to predict forecasting becomes less effective.