ML. Transforms. Time Series Namespace
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Namespace containing time-series data transformation components.
The is the wrapper to Microsoft.ML.Transforms.TimeSeries.IidAnomalyDetectionBaseWrapper.IidAnomalyDetectionBase that computes the p-values and martingale scores for a supposedly i.i.d input sequence of floats. In other words, it assumes the input sequence represents the raw anomaly score which might have been computed via another process.
Detect a signal change on an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimation and martingales.
Detect a signal spike on an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimation.
Detect anomalies in time series using Spectral Residual(SR) algorithm
The wrapper to Microsoft.ML.Transforms.TimeSeries.SsaAnomalyDetectionBaseWrapper.SsaAnomalyDetectionBase that implements the general anomaly detection transform based on Singular Spectrum modeling of the time-series. For the details of the Singular Spectrum Analysis (SSA), refer to http://arxiv.org/pdf/1206.6910.pdf.
Detect change points in time series using Singular Spectrum Analysis.
The wrapper to Microsoft.ML.Transforms.TimeSeries.SsaForecastingBaseWrapper.SsaForecastingBase that implements the general anomaly detection transform based on Singular Spectrum modeling of the time-series. For the details of the Singular Spectrum Analysis (SSA), refer to http://arxiv.org/pdf/1206.6910.pdf.
Forecasts using Singular Spectrum Analysis.
Detect spikes in time series using Singular Spectrum Analysis.
A class that runs the previously trained model (and the preceding transform pipeline) on the in-memory data, one example at a time. This can also be used with trained pipelines that do not end with a predictor: in this case, the 'prediction' will be just the outcome of all the transformations.
Growth ratio. Defined as Growth^(1/TimeSpan).
The side of anomaly detection.
The type of the martingale.
Ranking selection method for the signal.