# 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. |

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