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kategorie
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pojęcia
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Density-based anomaly detection

Approach that identifies anomalies as points located in low-density regions compared to the rest of the data, using algorithms such as LOF or DBSCAN.

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Real-time anomaly detection

Continuous process of identifying anomalies in instantaneous data streams, requiring low-latency and high-performance algorithms.

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Multivariate anomalies

Abnormal deviations detected when considering multiple variables simultaneously, which may go unnoticed in univariate analysis.

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Contextual anomalies

Observations that are abnormal only in a specific context, such as a high sale during normal periods but low during sales.

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Collective anomalies

Set of observations that are normal individually but abnormal when they appear together in a sequence or group.

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Grubbs' test

Statistical hypothesis test for detecting a single outlier in a normally distributed dataset.

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Deep learning-based anomaly detection

Use of deep neural networks such as autoencoders, GANs, or LSTMs to model complex patterns and identify deviations.

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Supervised anomaly detection

Approach using labeled data (normal/anomalous) to train classification models such as logistic regression or random forests.

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Métrique de reconstruction

Erreur quadratique moyenne ou autre mesure de divergence entre les données originales et leur reconstruction par un modèle, utilisée pour quantifier l'anormalité.

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