Forecast Assumption and Model Drift Challenge

Challenge whether a forecast is fit for a specific operating, financial, capacity, or investment decision. Test its assumptions, data, backtest behavior, structural stability, and sensitivity without presenting an unexecuted model run as fact.

Provide:
- Decision, forecast target, horizon, granularity, update cadence, required accuracy, and consequence of error: [Forecast decision and horizon]
- Model/formula, feature or driver logic, transformations, assumptions, overrides, constraints, and version: [Model logic and assumption register]
- Historical observations, training/calibration window, holdouts, backtests, errors, residuals, revisions, and benchmark forecasts: [Historical data and backtest evidence]
- Recent driver changes, regime shifts, policy/market/process changes, anomalies, missingness, and leading indicators: [Current driver and structural-change evidence]
- Base, upside, downside, stress cases, decision thresholds, risk limits, and alternative actions: [Scenarios thresholds and constraints]
- Forecast owner, data owner, finance or planning reviewer, decision owner, and analysis limitations: [Accountable owners and review limits]

Do not invent forecast values, confidence intervals, driver effects, or backtest statistics. Distinguish model output, planner override, observed fact, assumption, and scenario. Do not infer causal drivers from correlation alone. When raw data or executable logic is absent, assess the evidence and specify required tests rather than claiming to have rerun the forecast.

Challenge:

1. Define the decision contract.
   State which decision the forecast informs, the timing, relevant error direction, tolerance, and alternative if evidence is insufficient.

2. Build an assumption register.
   Record every decision-critical assumption, source, rationale, range, dependency, owner, last validation, and failure consequence. Include missing-data treatment, overrides, external drivers, capacity limits, and behavioral responses.

3. Review data and model lineage.
   Trace target and drivers through sources, transformations, update timing, revisions, exclusions, and version changes. Flag leakage, survivorship, regime mixing, inconsistent definitions, and unavailable lineage.

4. Assess backtest evidence.
   Compare forecast errors across time, horizon, segment, regime, and relevant benchmark using supplied results. Check bias, variance, tail misses, turning points, coverage of intervals, and revision behavior. Avoid extrapolating from one period.

5. Identify structural drift.
   Map changes that weaken learned relationships or operational assumptions. Separate observed drift signals from hypotheses and specify evidence that would confirm them.

6. Run or design sensitivity analysis.
   Where supplied calculations permit, show how decision outcomes change across plausible assumptions. Otherwise provide exact scenario runs needed. Identify tipping points and assumptions that do not affect the decision.

7. Compare decision robustness.
   Determine whether Base, Upside, Downside, Stress, delayed-decision, staged-commitment, or no-action cases cross material thresholds. Keep probability claims evidence-bound.

8. Make a fitness decision.
   Choose Fit for stated decision, Fit with range/conditions, Use only as scenario input, Recalibrate/rebuild, or Not decision-fit. Assign owner actions and refresh triggers.

Use ChatGPT to analyze supplied forecast artifacts and results; do not claim that backtests, refits, or sensitivity runs were executed unless their outputs are provided. Protect sensitive financial or operational data and stop where access or methodology is unauthorized. The model owner validates specification evidence, the finance or decision owner accepts decision risk, and the release owner authorizes operational use. Require acceptance evidence for each challenge: expected observation, actual observation, sensitivity boundary, reconciliation, and rollback or reforecast trigger.

Required deliverable:

# Forecast Assumption and Model Drift Challenge

## Decision Contract
- Forecast and horizon:
- Decision/threshold:
- Cost of over-forecast and under-forecast:
- Evidence limitations:

## Assumption Register
| Assumption | Value/range | Evidence/source | Dependency | Last validated | Sensitivity | Owner |
|---|---|---|---|---|---|---|

## Backtest and Drift Findings
| Period/slice | Error evidence | Bias/tail issue | Structural change | Confidence | Decision impact |
|---|---|---|---|---|---|

## Scenario and Tipping-Point Table
| Scenario/assumption change | Forecast effect supplied or test needed | Decision threshold crossed? | Evidence | Action |
|---|---|---|---|---|

## Forecast Fitness Decision
- Decision:
- Valid use and range:
- Prohibited overclaim:
- Required recalibration/data:
- Monitoring and refresh trigger:
- Accountable decision owner:

Completion requires a traceable assumption register, honest use of backtest evidence, explicit structural-drift risks, and a decision that remains within the tested horizon and data regime.