# Evaluate a statistical forecast's accuracy and explain the misses

A backtest and error review of a demand or revenue forecast against simple benchmarks, showing where it misses and how to improve it.

- Topic: Data & Analysis
- Framework: Few-Shot
- Level: Standard
- Tags: Data & Analytics, Analyze, Explain & Teach, Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/evaluate-a-statistical-forecast-s-accuracy-and-explain-the-misses

## Prompt

```text
Help me evaluate how accurate a forecast has been and explain where and why it missed.

**What is forecast:** [e.g. "weekly demand by SKU", "monthly revenue by region"]
**Forecast method:** [e.g. moving average, ARIMA, Prophet, ML model, manual planner adjustments]
**Data:** [PASTE forecast vs. actuals by period and segment, with the forecast date and horizon]
**How the forecast is used:** [e.g. "inventory orders placed 6 weeks ahead"]

Steps:
1. Choose error metrics that fit the use and explain the choice: MAE, MAPE (and why it breaks near zero), weighted MAPE, RMSE, and bias (systematic over- or under-forecasting).
2. Calculate or show me how to calculate error by horizon, by segment, and over time.
3. Compare against simple benchmarks: naive (last value), seasonal naive, and moving average. A forecast that cannot beat these needs rethinking.
4. Find patterns in the misses: specific segments, promotions, holidays, new products, structural breaks, data issues.
5. Separate model error from the effect of manual overrides, if any.
6. If the method is being changed, describe a proper backtest with rolling-origin evaluation.

Return:
- An accuracy summary table: Segment | Horizon | Error metric | Bias | vs. benchmark.
- The 3-5 biggest sources of error, with evidence.
- Specific improvements ranked by expected impact and effort.
- A short summary for business stakeholders on how much to trust the forecast and the right size of safety buffer.

Use only the numbers I provide. Mark anything I need to supply as [ADD DATA].
```

## What to fill in

- e.g. "weekly demand by SKU", "monthly revenue by region"
- e.g. "inventory orders placed 6 weeks ahead"
- ADD DATA

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