> For the complete documentation index, see [llms.txt](https://docs.flowlity.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flowlity.com/how-tos/investigate-demand-anomalies.md).

# Investigate and fix demand anomalies

This guide walks you through identifying demand anomalies, reviewing the corrections proposed by the Demand Anomaly Agent, and deciding whether to accept or reject them.

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**Spot anomalies via the Outliers view**

Open the [Demand](/modules/demand.md) module and switch to the **Outliers** view tab. This built-in view surfaces products where the Demand Anomaly Agent has detected unusual demand patterns.

Browse the list and click on a product to open its detail view.
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**Compare cleaned vs raw demand**

In the product detail chart, toggle on both:

* **Cleaned past demand** — The demand after Flowlity has removed anomalies.
* **Raw past demand** — The original, unmodified demand data.

Periods where the two lines diverge are where the agent proposed corrections. The **Anomaly adjustments impact** row in the data table shows the exact difference per period.
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**Review agent suggestions**

Go to the [Agents](/modules/agents.md) module and open the **Demand Anomaly Agent** card. The "How it works" tab shows a summary of all recent adjustments, broken down by type:

* **Outliers** — Unusual peaks.
* **Shortages** — Zero-demand periods caused by stockouts.
* **Smooth past promotions** — Promotional spikes smoothed out.

Each adjustment has a status: **Accepted**, **Rejected**, **Not treated**, or **Modified**.
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**Accept or reject corrections**

For each anomaly, decide whether the agent's correction is appropriate:

* **Accept** — The correction replaces the raw demand in cleaned past demand, improving future forecast accuracy.
* **Reject** — The original raw value is kept. Use this when the anomaly reflects genuine demand (e.g. a real one-time order, not noise).
* **Modify** — Adjust the agent's proposed value to a number you consider more accurate.
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**Configure automatic vs manual handling**

In the Agents module, open the Demand Anomaly Agent's **Configuration** tab. You can:

* **Activate or deactivate anomaly types** — Turn off detection for specific types if they aren't relevant.
* **Automatic vs manual** — Set whether corrections are accepted automatically or held for manual review.

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For products with sparse demand history, the Similar Products agent can help bootstrap a more reliable forecast. Check the product's forecast strategy in [Products settings](/settings/products.md) to see if similar products are configured.
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### Related pages

* [Forecast events and demand adjustments](/concepts/forecast-events.md) — How anomaly corrections and forecast events interact.
* [Demand forecasting explained](/concepts/demand-forecasting.md) — How cleaned demand feeds into the AI model.
* [Demand](/modules/demand.md) — Where anomalies are visible in the product detail view.
* [Agents](/modules/agents.md) — Where to review and configure the Demand Anomaly Agent.