Method
Probabilistic forecasting
Represent ranges and likelihoods alongside the central forecast.
Probabilistic forecasting explained
What it is
Estimate the point summaries and uncertainty the decision needs. Make clear which distributional information is available and what remains unspecified.
Why it matters
A mean or median can be useful for a particular decision. Decisions sensitive to downside or service risk may also need quantiles or joint scenarios; a few quantiles do not specify a full distribution.
Where planning teams fail
Teams plan against the mean, get surprised by the tails, and over- or under-invest because the forecast never told them how uncertain it was.
How RabbitHawk applies it
RabbitHawk produces the probabilistic outputs needed by the decision, with reliability evaluated at supported horizons and groups. We disclose assumptions and evidence limits; marginal coverage does not establish joint company risk.
Related methods
Keep exploring
- hierarchical reconciliation
Hierarchical reconciliation
Make forecasts coherent across the specified aggregation structure.
- forecast bias diagnostics
Forecast bias diagnostics
Find the systematic errors quietly distorting your plans.
- context aware forecasting
Context-aware forecasting
Bring new human and agent-detected evidence into the forecast.
Apply this to your data
See how RabbitHawk would use this method to improve your decisions.
Scope a forecasting diagnostic