How the number gets made
Each method assumes something about the world. The assumption, not the arithmetic, is what fails.
Every Method Assumes Something
Each forecasting method is an assumption about how the world behaves. Naming the assumption is how a method is chosen and how its failures are recognised.
The Average, and When It Is Enough
A weighted average of recent comparable periods is adequate for more workforce forecasting than anybody admits. When it is enough, how to set the window, and what it cannot do.
Trend, and the Line That Keeps Going
Fitting a trend is the commonest way to produce a large far-horizon error. What a real trend looks like, why trends flatten, and how damping fixes most of it.
Decomposition: Level, Trend, Season
Splitting a series into level, trend, season and remainder makes each component inspectable and each failure attributable. What the parts mean and how to read them.
Regression, and the Driver You Can Actually Get
A driver-based forecast is only as good as the driver's own forecast. The question that decides whether regression is worth it, and the drivers that pass it.
Why the Simple Method Usually Wins
Complex methods beat simple ones in-sample and rarely out of sample. The three reasons, and the conditions under which complexity does pay.
Choosing a Method by Testing It
A short procedure for choosing between forecasting methods on your own data, the mistakes that invalidate the test, and what to do with the result.