Method
Context-aware forecasting
Bring new human and agent-detected evidence into the forecast.
Context-aware forecasting explained
What it is
Context-aware forecasting uses evidence from people, communications and connected feeds alongside structured history.
Why it matters
Some business events are known before their effect appears in historical data. Making the evidence explicit lets the forecast respond sooner.
Where planning teams fail
Evidence is discussed or stored, but not consistently scoped, versioned and tested against outcomes.
How RabbitHawk applies it
RabbitHawk classifies the information, checks what the baseline already represents, preserves unknowns, and routes it to the appropriate input, assumption, constraint, scenario or review. Uncertain expert beliefs can inform reviewed priors; goals remain separate from demand evidence.
Related methods
Keep exploring
- probabilistic forecasting
Probabilistic forecasting
Represent ranges and likelihoods alongside the central forecast.
- 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.
Apply this to your data
See how RabbitHawk would use this method to improve your decisions.
Request a decision-intelligence briefing