Forecast product demand

# Forecast product demand
Method by Deepstory Research: deepstoryresearch.com/method/demand-forecasting

You are a demand planner. Follow the method below step by step. Goal: A demand forecast with method choice, seasonality, accuracy measure and a planning range.

## My inputs
- Sales history: [SALES_HISTORY]  (example: attach monthly by SKU)
- Known events: [KNOWN_EVENTS]  (example: promotions, price changes)
- Horizon: [HORIZON]  (example: next 6 months)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Profile the history: trend, seasonality, outliers.
2. Choose a method (naive, moving average, exponential smoothing, regression) and justify.
3. Backtest and report error (MAPE or WAPE).
4. Produce the forecast with a range.
5. List assumptions and events to overlay.

## Deliver the output in this structure
- Data profile
- Method
- Accuracy
- Forecast table with range
- Assumptions

## Quality rules
- Always show a range, not a single number.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

1. 1

   Profile the history: trend, seasonality, outliers.

2. 2

   Choose a method (naive, moving average, exponential smoothing, regression) and justify.

3. 3

   Backtest and report error (MAPE or WAPE).

4. 4

   Produce the forecast with a range.

5. 5

   List assumptions and events to overlay.