It's like trying to model a random graph using a set of distinct y=f(x) functions. There will be a major f(x) that captures most of the movement of the graph, b

It's like trying to model a random graph using a set of distinct y=f(x) functions. There will be a major f(x) that captures most of the movement of the graph, but the spikes and other weird happenings can be modelled by another function h(x), which however, does not describe the main trajectory of the initial function, because it's describing another effect. For example f(x) could measure the success of a film based on the amount of marketing money spent on it. h(x) would describe the success based on the day that it was released. f(x) does not consider h(x), so there would be small bumps that are dependent on the day it was released. When h(x), which by itself would be a very poor predictor, seeing as it only factors in the day of the week. But combined! They fix each other.