Add-DetectSpikeBySsaTransform.md

Add-DetectSpikeBySsaTransform

Detect spikes in time series data using singular spectrum analysis (SSA).

Description

Detect spikes in time series data using singular spectrum analysis (SSA).

Syntax

Add-DetectSpikeBySsaTransform [-OutputColumn] <String> [-InputColumn] <String> [-Confidence] <Double> [-PValueHistoryLength] <Int32> [-TrainingWindowSize] <Int32> [-SeasonalityWindowSize] <Int32> [-Side <AnomalySide>] [-ErrorFunction <ErrorFunction>] [-AppendTo <EstimatorChain<ITransformer>>] [-AppendScope <TransformerScope>] [-Context <MLContext>] [<CommonParameters>]

Parameters

-OutputColumn

Name of the column resulting from the transformation of inputColumnName. The column data is a vector of Double. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.

Type: System.String
Required: True
Position: 0
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-InputColumn

Name of column to transform. The column data must be Single. If set to null, the value of the outputColumnName will be used as source.

Type: System.String
Required: True
Position: 1
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-Confidence

The confidence for spike detection in the range [0, 100].

Type: System.Double
Required: True
Position: 2
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-PValueHistoryLength

The size of the sliding window for computing the p-value.

Type: System.Int32
Required: True
Position: 3
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-TrainingWindowSize

The number of points from the beginning of the sequence used for training.

Type: System.Int32
Required: True
Position: 4
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-SeasonalityWindowSize

An upper bound on the largest relevant seasonality in the input time-series.

Type: System.Int32
Required: True
Position: 5
Default value: None
Accept pipeline input: False
Accept wildcard characters: False

-Side

The argument that determines whether to detect positive or negative anomalies, or both.

Type: Microsoft.ML.Transforms.TimeSeries.AnomalySide
Required: False
Position: named
Default value: TwoSided
Accept pipeline input: False
Accept wildcard characters: False

-ErrorFunction

The function used to compute the error between the expected and the observed value.

Type: Microsoft.ML.Transforms.TimeSeries.ErrorFunction
Required: False
Position: named
Default value: SignedDifference
Accept pipeline input: False
Accept wildcard characters: False

-AppendTo

Append the created estimator to the end of this chain.

Type: Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer>
Required: False
Position: named
Default value: null
Accept pipeline input: True (ByValue)
Accept wildcard characters: False

-AppendScope

The scope allows for 'tagging' the estimators (and subsequently transformers) in the chain to be used 'only for training', 'for training and evaluation' etc.

Type: Microsoft.ML.Data.TransformerScope
Required: False
Position: named
Default value: Everything
Accept pipeline input: False
Accept wildcard characters: False

-Context

The context on which to perform the action. If omitted, the current (cached) context will be used.

Type: Microsoft.ML.MLContext
Required: False
Position: named
Default value: Current context
Accept pipeline input: False
Accept wildcard characters: False

Common parameters

This cmdlet supports the common parameters: Verbose, Debug, ErrorAction, ErrorVariable, WarningAction, WarningVariable, OutBuffer, PipelineVariable, and OutVariable. For more information, see about_CommonParameters.

Inputs

Type	Description
Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer>	You can pipe the EstimatorChain to append to this cmdlet.

Outputs

Type	Description
Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer>	This cmdlet returns the appended EstimatorChain.