머신러닝 이상 감지기

시계열 머신러닝 이상 감지기의 생성 및 워크플로

이 머신러닝 이상 감지기는 Statistics and Machine Learning Toolbox™의 비지도 학습 감지 알고리즘을 기반으로 합니다. 이러한 알고리즘은 데이터 세트 내에서 이상값을 식별하기 위해 다양한 메서드를 사용합니다.

머신러닝 알고리즘은 대체로 처리 속도가 빠르며, 데이터에 적합한 감지기를 찾는 과정을 시작할 때 먼저 고려해 볼 만한 좋은 감지기입니다.

앱

Interactively create, train, test, and tune detectors for detecting anomalous behavior in time series (R2026a 이후)

`timeSeriesIforestAD`	Create a machine learning isolation forest anomaly detector model for time series data (R2026a 이후)
`timeSeriesRrcforestAD`	Create a machine learning robust random cut forest anomaly detector model for time series data (R2026a 이후)
`timeSeriesLofAD`	Create a machine learning local outlier factor model for anomaly detection in time series data (R2026a 이후)
`timeSeriesOcsvmAD`	Create a machine learning one-class SVM anomaly detector model for time series data (R2026a 이후)

`train`	Train time series machine learning anomaly detector and obtain detection threshold (R2026a 이후)
`detect`	Detect anomalies in time series using a trained time series machine learning detector model (R2026a 이후)
`updateDetector`	Update settings of a trained time series machine learning anomaly detector and recompute detection threshold (R2026a 이후)

`plotHistogram`	Plot histogram of anomaly scores and detection threshold for trained machine learning anomaly detector (R2025a 이후)
`plot`	Plot detected anomalies and anomaly scores generated from trained machine learning anomaly detectors (R2026a 이후)
`timeSeriesAnomalyMetrics`	Compute specialized evaluation metrics for time series anomaly detection (R2026a 이후)

`TimeSeriesIForestDetector`	Detect subsequence anomalies in time series using an isolation forest algorithm (R2026a 이후)
`TimeSeriesRRCForestDetector`	Detect subsequence anomalies in time series using a robust random cut forest algorithm (R2026a 이후)
`TimeSeriesLOFDetector`	Detect subsequence anomalies in time series using a local outlier factor algorithm (R2026a 이후)
`TimeSeriesOCSVMDetector`	Detect subsequence anomalies in time series using a one-class SVM detector (R2026a 이후)

Detecting Anomalies in Time Series
Examine the general workflow for developing anomaly detectors that detect anomalous subsequences in time series.
Train and Test Isolation Forest Time Series Anomaly Detector
This example shows the development cycle for a Machine Learning anomaly detector.
Interpret Evaluation Metrics for Time Series Anomaly Detectors
Interpret evaluation metrics that are returned by the evaluationMetrics function and the app.