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.
Every forecasting method is an assumption about how the world behaves, expressed as arithmetic. An average assumes the level is stable. A trend assumes change continues at the same rate. A seasonal model assumes last year's shape repeats. A regression assumes a relationship holds.
The method in “Every Method Assumes Something” is only as useful as the definitions and source records behind it. A team exploring a structured route to ways to track work hours for ways to track work hours can add timely work and project context, with transparent settings, limited access and a documented correction route around every consequential use.
Methods fail when their assumption stops being true, not when their arithmetic is wrong, and the arithmetic is where all the attention goes.
As a separate benchmark for “Every Method Assumes Something”, see Snowflake data resources. Keep source data, model, override and resulting decision visible as distinct steps.
The assumption is the thing to choose
Not the algorithm. "Is the level stable, or is it moving?" is a question about the operation that somebody can answer. "Should we use exponential smoothing or ARIMA?" is a question about software.
"Is the level stable, or is it moving?" is a question about the operation, and somebody can answer it. "Exponential smoothing or ARIMA?" is a question about software, and it follows from the first.
The first question determines the second and is almost never asked first.
The four common assumptions
The level is stable. The level is moving at a roughly constant rate. The within-year shape repeats. A measurable external thing moves demand.
Most workforce forecasting is built on one or two of these, and saying which is the whole of method selection at the level that matters.
Writing the assumption down
One sentence per forecast: "Assumes the weekly shape from the last ten weeks continues and the level is flat; no trend fitted."
That sentence tells the next person what to check when it stops working, and it is checkable in a way that a model name is not.
How to tell which assumption fits
Plot the series. A stable level with noise around it argues for an average. A persistent slope argues for a trend. A repeating within-year shape argues for seasonal decomposition.
Looking at the plot takes five minutes and is skipped remarkably often.
The assumption that is usually wrong
That the trend continues. Trends in service demand are usually driven by something finite — a migration, a product ramp, a market entry — and they flatten.
A linear trend extrapolated four quarters out is the most common source of large far-horizon error.
Damping
The standard remedy: let the trend decay with distance rather than continuing at full strength. It is a small change to most methods and it reflects what trends actually do.
The damping rate is itself an assumption and it should be stated.
When several assumptions apply
Combine explicitly rather than reaching for a method that does everything. Level plus season plus named events, each visible, is easier to diagnose than one fitted object.
The components can be wrong individually and found individually, which is the practical advantage.
The assumption behind a machine-learned model
That the future resembles the training period in every respect that mattered, including the ones nobody identified. That is a much stronger assumption than the simple methods make, not a weaker one.
Where the operation is stable and the data is plentiful, it can be right and very effective. Where there has been a break, it is confidently wrong and cannot explain itself.
Assumptions that are untestable at the horizon
At a year, almost all of them. There is no out-of-sample test with enough observations to distinguish methods at that distance.
This argues for simplicity at the far horizon and for scenarios rather than fitted precision.
Failure modes follow from assumptions
A stable-level method fails by lagging a real change. A trend method fails by overshooting when the trend flattens. A seasonal method fails when the calendar shifts.
Knowing which method is running tells you which failure to watch for, which is what the error review is actually looking at.
Changing the assumption
Requires a reason, and the reason belongs in the record: "trend added from June, after four quarters of consistent growth attributable to the new contract."
A method changed without a recorded reason is indistinguishable, six months later, from a method changed to make a number look better.
Assumptions that conflict
Worth checking when methods are combined. A trend component that assumes continued growth sitting alongside a damping rule that assumes flattening are two statements about the same thing.
Where they conflict, the forecast is a compromise nobody chose, and writing both assumptions out in one sentence usually reveals it.
The assumption a reader can challenge
The real test of writing it down: can somebody outside the planning function disagree with it. "Assumes the weekly shape from the last ten weeks continues" invites a useful objection from anybody who knows the operation changed last month.
A method name invites no objection at all, which is why it feels safer and is not.
The question at the end
Whether the assumption is still a fair description of the operation. That question is answered by somebody who knows the operation, not by a test statistic, and it is the review that catches structural change before the error record does.