Anomaly detection in data
Detect anomalies and outliers in a dataset with multiple statistical methods and severity scoring.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Je dois détecter des anomalies dans mon dataset [NOM_DATASET] contenant [NOMBRE_OBSERVATIONS] enregistrements et [NOMBRE_VARIABLES] variables. Les données représentent [DESCRIPTION] sur la période [PERIODE].
Type d'anomalies recherchées : [TYPE_ANOMALIES] (ex : fraudes transactions, pannes équipements, comportements clients inhabituels, erreurs de saisie)
Mets en place une détection d'anomalies complète :
- Analyse univariée : Z-score et IQR pour identifier les valeurs aberrantes par variable
- Analyse multivariée : Isolation Forest ou Local Outlier Factor pour les anomalies contextuelles
- Analyse temporelle : détection des ruptures de série et des pics inhabituels
- Règles métier : [REGLES_METIER] à implémenter comme filtres complémentaires
- Scoring des anomalies avec niveau de sévérité (critique, warning, info)
- Visualisation des anomalies détectées sur les dimensions clés
- Recommandations pour investiguer les 10 anomalies les plus critiques
Seuil d'alerte souhaité : [SEUIL_FAUX_POSITIFS] (ex : 1% de faux positifs acceptable)
Outil : [OUTIL] (Python, SQL, Excel)
Explique comment ajuster la sensibilité du système.
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Why this prompt works
<p>The prompt is effective because it combines univariate, multivariate, and business rule methods, covering all possible types of anomalies. The severity scoring concept enables immediate prioritization of investigations.</p>
Use Cases
Expected Output
Multi-method detection system with scored anomalies, visualizations, and investigation recommendations.
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- LéaAI
Pour réduire les faux positifs, combinez Isolation Forest et LOF en un score moyen pondéré, puis ajustez le seuil de décision sur un échantillon labellisé. Pensez aussi à normaliser les variables avant l'analyse multivariée.
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