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Anomaly detection overviewAnomaly detection is a data mining technique that you can use to identify data deviations in a given dataset. For example, if the return rate for a given product increases substantially from the baseline for that product, that might indicate a product defect or potential fraud. You can use anomaly detection to detect critical incidents, such as technical issues, or opportunities, such as changes in consumer behavior.
One challenge when you use anomaly detection is determining what counts as anomalous data. If you have labeled data that identifies anomalies, you can perform anomaly detection by using the ML.PREDICT function with one of the following supervised machine learning models:
If you aren't certain what counts as anomalous data, or you don't have labeled data to train a model on, you can use unsupervised machine learning to perform anomaly detection. Use the ML.DETECT_ANOMALIES function with one of the following models to detect anomalies in training data or new serving data:
By using the default settings in the CREATE MODEL statements and the inference functions, you can create and use an anomaly detection model even without much ML knowledge. However, having basic knowledge about ML development helps you optimize both your data and your model to deliver better results. We recommend using the following resources to develop familiarity with ML techniques and processes:
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[[["Anomaly detection is a data mining technique used to identify deviations in datasets, which can signal product defects, fraud, or changes in consumer behavior."],["If you have labeled data, supervised machine learning models like linear regression, boosted trees, random forest, DNN, Wide & Deep, and AutoML models can be used with the `ML.PREDICT` function for anomaly detection."],["When you lack labeled data or are uncertain about what constitutes anomalous data, unsupervised machine learning can be employed with the `ML.DETECT_ANOMALIES` function."],["The `ML.DETECT_ANOMALIES` function supports various model types, including ARIMA_PLUS, ARIMA_PLUS_XREG, K-means, Autoencoder, and PCA, each suited for different data types such as time series or independent and identically distributed random variables."],["Basic knowledge of ML can enhance anomaly detection results, and resources such as the Machine Learning Crash Course, Intro to Machine Learning, and Intermediate Machine Learning are recommended to develop this knowledge."]]],[]]
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