IidSpikeEstimator Class
Definition
Important
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Detect a signal spike on an independent identically distributed (i.i.d.) time series based on adaptive kernel density estimation.
public sealed class IidSpikeEstimator : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.TimeSeries.IidSpikeDetector>type IidSpikeEstimator = class
inherit TrivialEstimator<IidSpikeDetector>Public NotInheritable Class IidSpikeEstimator
Inherits TrivialEstimator(Of IidSpikeDetector)- Inheritance
Remarks
To create this estimator, use DetectIidSpike.
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? | No |
| Input column data type | Single |
| Output column data type | 3-element vector ofDouble |
| 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 trainer assumes that data points collected in the time series are independently sampled from the same distribution (independent identically distributed). Thus, the value at the current timestamp can be viewed as the value at the next timestamp in expectation. If the observed value at timestamp $t-1$ is $p$, the predicted value at $t$ timestamp would be $p$ as well.
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.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| GetOutputSchema(SchemaShape) |
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. |