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Documentation Index

Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt

Use this file to discover all available pages before exploring further.

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What Multi Yhat is

Multi Yhat is DataGenie’s multi-model anomaly detection framework. A single KPI is evaluated using multiple Anomaly Detection (AD) Groups in parallel — each producing its own expected value (yhat) and anomaly interpretation — so you can compare forecast-based, business-relative, and reference-based detection side by side. All AD models are deterministic — the same inputs always produce the same expected values and anomaly flags. Multi Yhat gives you flexibility across baselines without sacrificing reproducibility. See Responsible AI.
Nirvana datasets inherit default AD Groups from their component datasets — so multi-source monitoring stays consistent without re-configuring detection per combined view.

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When to use Multi Yhat

  • You want to compare forecast-based vs business-relative logic
  • You need QoQ, YoY, rolling average, or fixed reference comparisons
  • You want more control over anomaly sensitivity
  • You need transparency into which detection logic flagged a change

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What you get

Multiple prediction paths

The same KPI can be evaluated using different anomaly detection groups.

Transparent anomaly logic

Clearly see which detection group produced the expected value and flagged the anomaly.

Business-aligned flexibility

Choose the prediction style that best fits the KPI and business context.

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How Multi Yhat works

1

Create or configure an Anomaly Detection Group

Each group represents one prediction logic (forecast, previous period, rolling average, fixed reference, etc.).
2

Models are evaluated inside the group

The system selects the best eligible model for that group.
3

One yhat is produced per group

The KPI can now have multiple expected values — one for each active group.
4

Insights reflect the selected group

Explorer, Top Stories, and Deep Dive use the chosen group to interpret anomalies.

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Anomaly Detection Groups (AD Groups)

An AD Group is a named configuration that combines one or more detection models and assigns them to your KPIs. You can have multiple AD Groups per dataset — useful for comparing detection sensitivity or testing a new model configuration without affecting your live setup.
1

Open your Dataset

Navigate to Datasets, select your dataset, and go to Configure your Detection Model under Build Your Insights.
2

Select a KPI

Use the KPI selector to choose the metric you want to configure detection for.
3

Create or select an AD Group

Use the DataGenie AD default group, or create a custom group by clicking + Add Group.
4

Add models to the group

Click + Add AD Model to add AutoETS, Prophet, or both.
5

Save configuration

Click Save Configuration. The updated detection setup takes effect on the next processing run.

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Where Multi Yhat appears

Dataset Configuration

Enable, disable, or edit anomaly detection groups and model parameters.

Explorer

Analyze KPI trends using a selected anomaly detection group.

Top Stories

Interpret anomalies and prediction behavior based on the active group.

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Typical workflows

  • Select the detection group that matches your business comparison logic.
  • Review how anomalies change under different groups.
  • Use Top Stories to align insights with your preferred detection style.

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Tips for better results

Not all KPIs behave well under one detection style. Use forecast models for trend-heavy KPIs and reference-based models for benchmark-style KPIs.
Create a second AD Group with adjusted sensitivity for your most volatile KPIs — e.g. a promotional SKU that regularly spikes. This way you can toggle between the default group (for baseline monitoring) and the custom group (for campaign analysis) without reconfiguring anything.

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What to do next

Top Stories

See how anomalies surface in prioritized insights.

Explorer

Analyze prediction behavior and dimensional trends.

Datasets Overview

Back to full dataset configuration.