# A forecast without a band is not a forecast: it is an opinion with decimals

> A bare number invites belief. A number with a band states how much is known and how much is not, and lets you check afterwards whether the band was right as often as it promised.

URL: https://raptia.co/en/blog/a-forecast-without-a-band-is-not-a-forecast
Publicado: 2026-09-22
Actualizado: 2026-09-22

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## What is missing from "this flock will weigh 2,640 g on day 40"?

What is missing is how much of that is knowledge and how much is ignorance.

That sentence is indistinguishable from one saying 2,641. The last two digits
are pure decoration: nobody knows a flock's weight to the gram twelve days out,
and presenting it that way invites decisions taken at a precision the figure
does not have.

The honest version is **2,420 – 2,860 g, centred on 2,640**. It takes more room
and says considerably more.

## Why does the band change the decision and not just the presentation?

Because which edge matters depends on what the number is for.

| Decision | Edge to read | Why |
|---|---|---|
| Committing kilos to a customer | the lower one | if you miss, you pay |
| Filling a truck or a plant slot | the upper one | if there is spare, people wait |
| Deciding whether a target is reachable | both | if the target falls inside, the honest answer is "unknown" |

That third case is the one most often abused. When the target sits **inside**
the band, any decisive verdict — "yes it will" or "no it won't" — is being
invented by the system. The third possible answer is not an excuse: it is the
actual state of knowledge, and some decisions change depending on which of the
three it is.

## What is the band made of?

Three things that add up, worth separating because they shrink in different
ways:

1. **Measurement error.** How many birds are weighed and how. It shrinks with
   better weighing: larger sample, calibrated scale, same time of day.
2. **Residual ignorance.** What the model does not capture about that
   particular farm's management. It shrinks with history: more closed cycles
   from the same complex.
3. **Distance to horizon.** Twelve days out is less knowable than two. It does
   not shrink at all; it is what makes the band widen as the projection reaches
   further.

And a fourth that almost nobody models: **the quality of the input data**. If
that week's weighings came in flagged — a weight that falls with no thinning to
explain it, a record identical to the previous day's, a capture outside the
shed's working hours — the band has to widen. A projection computed on doubtful
data with the same tightness as on clean data is lying twice.

## How do you know whether a band is any good?

You check, and the check is deliberately uncomfortable.

An 80% band promises to contain the real value 80% of the time. No more, no
less. You have to store every projection with the date it was made and the day
it points at, wait for that day, record the actual value and count.

- **Coverage well below what was promised**: the band is too tight. The system
  appears to know more than it does, which is the fastest way to lose a
  customer's trust.
- **Coverage well above**: the band is so wide it carries no information.
  "Between 1,800 and 3,400 g" is always right and helps decide nothing.

That report has to be showable even when it looks bad. Ask anyone selling you
forecasts for their coverage: if they cannot show it to you, what they are
asking for is an act of faith.

## And when there is no history yet?

That is the real test, because it is precisely when the projection is looked at
most and known least.

The right approach is to start from the genetic standard adjusted by whatever
little has been observed of the flock, and **shrink** that adjustment back
towards the standard the weaker the evidence. With two weighings the system
should lean mostly on the reference curve; with twelve, on the flock.

The wrong approach is to train a model on three closed cycles and present it as
learning. With three flocks you can fit anything to anything. An honest engine
has a gatekeeper: if the trained model does not consistently beat the standard
under cross-validation, it is **rejected, and the screen says it was rejected**.
Rejecting more often than adopting is not a failure of the mechanism: it is the
mechanism.

## The rule, in one line

If a system hands you a number with no band, the question to ask is not "how
wrong are you?". It is "how often have you been right so far, and where can I
see it?". If there is no answer, it was not a forecast.

In the demonstration you can see how every projection is recorded against the
value that was later measured.