その他の異常検出器

その他の異常検出器の作成とワークフロー

統計的工程制御検出器は、Statistics and Machine Learning Toolbox™ で提供される産業用制御の手法を組み込んだものです。この検出手法では、モデルを使用する代わりに、統計データを使用して外れ値を識別します。

機械学習モデルと同様に、統計的工程制御検出器は学習が高速であり、検出器を開発する良い開始点となります。

アプリ

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

`timeSeriesSpcAD`	Create an anomaly detector that applies statistical process control techniques to time series data (R2026a 以降)
`TimeSeriesSPCDetector`	Detect subsequence anomalies in time series using statistical process control (R2026a 以降)

`train`	Train statistical process control (SPC) anomaly detector and obtain detection threshold (R2025a 以降)
`detect`	Detect anomalies in time series using a trained time statistical process control (SPC) detector model (R2026a 以降)
`updateDetector`	Update settings of a trained statistical process control anomaly detector (R2026a 以降)

`plotHistogram`	Plot histogram of anomaly scores and detection threshold for statistical process control (SPC) anomaly detector (R2025a 以降)
`plot`	Plot detected anomalies and anomaly scores generated from time series anomaly detectors that are based on statistical process control (R2026a 以降)
`timeSeriesAnomalyMetrics`	Compute specialized evaluation metrics for time series anomaly detection (R2026a 以降)

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