SsaSpikeEstimator Class


Detect spikes in time series using Singular Spectrum Analysis.

public sealed class SsaSpikeEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.TimeSeries.SsaSpikeDetector>
type SsaSpikeEstimator = class
    interface IEstimator<SsaSpikeDetector>
Public NotInheritable Class SsaSpikeEstimator
Implements IEstimator(Of SsaSpikeDetector)


To create this estimator, use DetectSpikeBySsa

Input and Output Columns

There is only one input column. The input column must be Single where a Single value indicates a value at a timestamp in the time series.

It produces a column that is a vector with 3 elements. The output vector sequentially contains alert level (non-zero value means a change point), score, and p-value.

Estimator Characteristics

Does this estimator need to look at the data to train its parameters? Yes
Input column data type Single
Output column data type 3-element vector of Double
Exportable to ONNX No

Estimator Characteristics

Machine learning task Anomaly detection
Is normalization required? No
Is caching required? No
Required NuGet in addition to Microsoft.ML Microsoft.ML.TimeSeries

Training Algorithm Details

This class implements the general anomaly detection transform based on Singular Spectrum Analysis (SSA). SSA is a powerful framework for decomposing the time-series into trend, seasonality and noise components as well as forecasting the future values of the time-series. In principle, SSA performs spectral analysis on the input time-series where each component in the spectrum corresponds to a trend, seasonal or noise component in the time-series. For details of the Singular Spectrum Analysis (SSA), refer to this document.

Anomaly Scorer

Once the raw score at a timestamp is computed, it is fed to the anomaly scorer component to calculate the final anomaly score at that timestamp.

Spike detection based on p-value

The p-value score indicates whether the current point is an outlier (also known as a spike). The lower its value, the more likely it is a spike. The p-value score is always in $[0, 1]$.

This score is the p-value of the current computed raw score according to a distribution of raw scores. Here, the distribution is estimated based on the most recent raw score values up to certain depth back in the history. More specifically, this distribution is estimated using kernel density estimation with the Gaussian kernels of adaptive bandwidth.

If the p-value score exceeds $1 - \frac{\text{confidence}}{100}$, the associated timestamp may get a non-zero alert value in spike detection, which means a spike point is detected. Note that $\text{confidence}$ is defined in the signatures of DetectIidSpike and DetectSpikeBySsa.

Check the See Also section for links to usage examples.



Train and return a transformer.


Schema propagation for transformers. Returns the output schema of the data, if the input schema is like the one provided.

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.

WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.

Applies to

See also