機械学習異常検出器

時系列機械学習異常検出器の作成とワークフロー

機械学習異常検出器は、Statistics and Machine Learning Toolbox™ の教師なし検出アルゴリズムに基づきます。これらのアルゴリズムでは、さまざまな方法を使用して一連のデータ内の外れ値を識別します。

機械学習アルゴリズムは比較的高速になる傾向があり、多くの場合、データに適した検出器を見つける過程で最初に試す検出器として便利です。

アプリ

時系列異常検出器Interactively create, train, test, and tune detectors for detecting anomalous behavior in time series (R2026a 以降)

関数

すべて展開する

timeSeriesIforestADCreate a machine learning isolation forest anomaly detector model for time series data (R2026a 以降)
timeSeriesRrcforestADCreate a machine learning robust random cut forest anomaly detector model for time series data (R2026a 以降)
timeSeriesLofADCreate a machine learning local outlier factor model for anomaly detection in time series data (R2026a 以降)
timeSeriesOcsvmADCreate a machine learning one-class SVM anomaly detector model for time series data (R2026a 以降)
trainTrain time series machine learning anomaly detector and obtain detection threshold (R2026a 以降)
detectDetect anomalies in time series using a trained time series machine learning detector model (R2026a 以降)
updateDetectorUpdate settings of a trained time series machine learning anomaly detector and recompute detection threshold (R2026a 以降)
plotHistogramPlot histogram of anomaly scores and detection threshold for trained machine learning anomaly detector (R2025a 以降)
plotPlot detected anomalies and anomaly scores generated from trained machine learning anomaly detectors (R2026a 以降)
timeSeriesAnomalyMetricsCompute specialized evaluation metrics for time series anomaly detection (R2026a 以降)
TimeSeriesIForestDetectorDetect subsequence anomalies in time series using an isolation forest algorithm (R2026a 以降)
TimeSeriesRRCForestDetectorDetect subsequence anomalies in time series using a robust random cut forest algorithm (R2026a 以降)
TimeSeriesLOFDetectorDetect subsequence anomalies in time series using a local outlier factor algorithm (R2026a 以降)
TimeSeriesOCSVMDetectorDetect subsequence anomalies in time series using a one-class SVM detector (R2026a 以降)

トピック