3 min read ·
Forecasting Definition: What a Forecast Actually Says
Forecasting means estimating future outcomes with available information. Learn how market prices express probabilities and how calibration tests them.
Forecasting is the process of estimating future events or quantities using the information available when the estimate is made. A forecast describes what may happen—not what someone wants to happen, and not a guarantee.
Hyndman and Athanasopoulos’s Forecasting: Principles and Practice defines forecasting as predicting the future as accurately as possible using available information, including historical data and knowledge of events that could affect the outcome. It distinguishes forecasting from goals, which describe desired outcomes, and planning, which determines actions.
In YES/NO prediction markets, the forecast is a probability attached to a precisely defined question.
What form can a forecast take?
A forecast does not have to name one outcome with certainty. These hypothetical examples illustrate three common forms:
| Form | Example | What it expresses |
|---|---|---|
| Point forecast | “Next month’s sales will be 10,000 units.” | A single estimated quantity |
| Interval forecast | “There is an 80% probability that sales will be between 8,000 and 12,000 units.” | A range with stated uncertainty |
| Event-probability forecast | “There is a 65% chance that sales will exceed 10,000 units.” | The likelihood of a defined event |
Forecasts can come from statistical models, structured judgment or market prices. Historical data are useful, but not mandatory: the forecasting textbook describes judgmental methods for situations where relevant past data are unavailable, as well as prediction intervals for expressing uncertainty. The appropriate method depends on the information available and how predictable the target is.
How a market price becomes a forecast
Consider a hypothetical contract asking: “Will Company A launch Product B by December 31, 2027?” Assume its rules specify what counts as a launch and which evidence determines settlement.
Suppose the YES contract pays $1 if the question resolves YES and $0 otherwise. If it trades at $0.65, the usual reading is a 65% market-implied probability of YES. The mechanism is straightforward: with a 65% chance of receiving $1, the expected payout is $0.65, before costs. Kalshi explains this relationship in its guide to event contracts.
That does not establish that the true chance is exactly 65%. It is a price-based estimate. Kalshi’s guide also explains bid–ask spreads and acknowledges that participants can have reasons to trade other than their probability beliefs, such as hedging. Record whether your number is a last trade, bid, ask or midpoint rather than treating them as interchangeable. For the fuller mechanism, see how prediction-market prices become probabilities.
How do you judge a forecast?
A 65% forecast leaves a 35% chance of NO. One NO resolution therefore does not, by itself, prove the probability was badly estimated.
Calibration asks whether events assigned similar probabilities occur at approximately those rates across a sample. The Met Office’s guide to reliability diagrams describes this by grouping forecasts into probability bins and comparing predicted probabilities with observed frequencies.
For example, in a hypothetical set of 100 comparable forecasts near 65%, about 65 YES outcomes would be consistent with good calibration. Exactly 65 is not required: sampling variation matters, and a small sample offers limited evidence.
Calibration is also not the whole evaluation. A forecast should help distinguish more likely events from less likely ones; Brier score versus calibration explains the distinction.
To make a probability forecast testable, save its question, resolution rule, probability and timestamp. When comparing markets with polls or pundits, align the event and forecasting horizon. An estimate made a month before resolution should not be judged against one made minutes beforehand as if both had the same information.