Microsoft.ML.Transforms.TimeSeries Namespace

Namespace containing time-series data transformation components.

Classes

AdaptiveSingularSpectrumSequenceModeler

This class implements basic Singular Spectrum Analysis (SSA) model for modeling univariate time-series. For the details of the model, refer to http://arxiv.org/pdf/1206.6910.pdf.

IidAnomalyDetectionBaseWrapper

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.

IidChangePointDetector

ITransformer resulting from fitting a IidChangePointEstimator.

IidChangePointEstimator

Detect a signal change on an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimation and martingales.

IidSpikeDetector

ITransformer resulting from fitting a IidSpikeEstimator.

IidSpikeEstimator

Detect a signal spike on an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimation.

PredictionFunctionExtensions
SrCnnAnomalyDetectionBase
SrCnnAnomalyDetector

ITransformer resulting from fitting a SrCnnAnomalyEstimator.

SrCnnAnomalyEstimator

Detect anomalies in time series using Spectral Residual(SR) algorithm

SsaAnomalyDetectionBaseWrapper

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.

SsaChangePointDetector

ITransformer resulting from fitting a SsaChangePointEstimator.

SsaChangePointEstimator

Detect change points in time series using Singular Spectrum Analysis.

SsaForecastingBaseWrapper

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.

SsaForecastingEstimator

Forecasts using Singular Spectrum Analysis.

SsaForecastingTransformer

ITransformer resulting from fitting a SsaForecastingEstimator.

SsaSpikeDetector

ITransformer resulting from fitting a SsaSpikeEstimator.

SsaSpikeEstimator

Detect spikes in time series using Singular Spectrum Analysis.

TimeSeriesPredictionEngine<TSrc,TDst>

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.

TimeSeriesPredictionFunction<TSrc,TDst>

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.

Structs

AdaptiveSingularSpectrumSequenceModeler.GrowthRatio

Growth ratio. Defined as Growth^(1/TimeSpan).

GrowthRatio

Growth ratio. Defined as Growth^(1/TimeSpan).

Enums

AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod

Ranking selection method for the signal.

AnomalySide

The side of anomaly detection.

ErrorFunction
MartingaleType

The type of the martingale.

RankSelectionMethod

Ranking selection method for the signal.