TimeSeriesCatalog.DetectSpikeBySsa Metodo
Definizione
Importante
Alcune informazioni sono relative alla release non definitiva del prodotto, che potrebbe subire modifiche significative prima della release definitiva. Microsoft non riconosce alcuna garanzia, espressa o implicita, in merito alle informazioni qui fornite.
Overload
| DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction) |
Creare SsaSpikeEstimator, che stima i picchi nelle serie temporali usando l'analisi di spettro singolare (SSA). |
| DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction) |
Obsoleti.
Creare SsaSpikeEstimator, che stima i picchi nelle serie temporali usando l'analisi di spettro singolare (SSA). |
DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)
Creare SsaSpikeEstimator, che stima i picchi nelle serie temporali usando l'analisi di spettro singolare (SSA).
public static Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);static member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * double * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator<Extension()>
Public Function DetectSpikeBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, pvalueHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference) As SsaSpikeEstimatorParametri
- catalog
- TransformsCatalog
Catalogo della trasformazione.
- outputColumnName
- String
Nome della colonna risultante dalla trasformazione di inputColumnName.
I dati della colonna sono un vettore di Double. Il vettore contiene 3 elementi: avviso (valore non zero significa un picco), punteggio non elaborato e p-value.
- inputColumnName
- String
Nome della colonna da trasformare. I dati della colonna devono essere Single.
Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine.
- confidence
- Double
Attendibilità per il rilevamento dei picchi nell'intervallo [0, 100].
- pvalueHistoryLength
- Int32
Dimensioni della finestra scorrevole per il calcolo del valore p.
- trainingWindowSize
- Int32
Numero di punti dall'inizio della sequenza usata per il training.
- seasonalityWindowSize
- Int32
Limite superiore sulla stagionalità più rilevante nella serie temporale di input.
- side
- AnomalySide
Argomento che determina se rilevare anomalie positive o negative o entrambe.
- errorFunction
- ErrorFunction
Funzione usata per calcolare l'errore tra il valore previsto e il valore osservato.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectSpikeBySsaBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify spiking points in the series. This estimator can account for
// temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a recurring pattern and a spike
// within the pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
//This is a spike.
new TimeSeriesData(100),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(SsaSpikePrediction.Prediction);
// The transformed data.
var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName,
inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// SsaSpikePrediction.
var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// 0 0 -2.53 0.50
// 1 0 -0.01 0.01
// 2 0 0.76 0.14
// 3 0 0.69 0.28
// 4 0 1.44 0.18
// 0 0 -1.84 0.17
// 1 0 0.22 0.44
// 2 0 0.20 0.45
// 3 0 0.16 0.47
// 4 0 1.33 0.18
// 0 0 -1.79 0.07
// 1 0 0.16 0.50
// 2 0 0.09 0.50
// 3 0 0.08 0.45
// 4 0 1.31 0.12
// 100 1 98.21 0.00 <-- alert is on, predicted spike
// 0 0 -13.83 0.29
// 1 0 -1.74 0.44
// 2 0 -0.47 0.46
// 3 0 -16.50 0.29
// 4 0 -29.82 0.21
}
private static void PrintPrediction(float value, SsaSpikePrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2]);
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
class SsaSpikePrediction
{
[VectorType(3)]
public double[] Prediction { get; set; }
}
}
}
Si applica a
DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)
Attenzione
This API method is deprecated, please use the overload with confidence parameter of type double.
Creare SsaSpikeEstimator, che stima i picchi nelle serie temporali usando l'analisi di spettro singolare (SSA).
[System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
public static Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);public static Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimatorstatic member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator<Extension()>
Public Function DetectSpikeBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, pvalueHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference) As SsaSpikeEstimatorParametri
- catalog
- TransformsCatalog
Catalogo della trasformazione.
- outputColumnName
- String
Nome della colonna risultante dalla trasformazione di inputColumnName.
I dati della colonna sono un vettore di Double. Il vettore contiene 3 elementi: avviso (valore non zero significa un picco), punteggio non elaborato e p-value.
- inputColumnName
- String
Nome della colonna da trasformare. I dati della colonna devono essere Single.
Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine.
- confidence
- Int32
Attendibilità per il rilevamento dei picchi nell'intervallo [0, 100].
- pvalueHistoryLength
- Int32
Dimensioni della finestra scorrevole per il calcolo del valore p.
- trainingWindowSize
- Int32
Numero di punti dall'inizio della sequenza usata per il training.
- seasonalityWindowSize
- Int32
Limite superiore sulla stagionalità più rilevante nella serie temporale di input.
- side
- AnomalySide
Argomento che determina se rilevare anomalie positive o negative o entrambe.
- errorFunction
- ErrorFunction
Funzione usata per calcolare l'errore tra il valore previsto e il valore osservato.
Restituisce
- Attributi
Esempio
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectSpikeBySsaBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify spiking points in the series. This estimator can account for
// temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a recurring pattern and a spike
// within the pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
//This is a spike.
new TimeSeriesData(100),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(SsaSpikePrediction.Prediction);
// The transformed data.
var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName,
inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// SsaSpikePrediction.
var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// 0 0 -2.53 0.50
// 1 0 -0.01 0.01
// 2 0 0.76 0.14
// 3 0 0.69 0.28
// 4 0 1.44 0.18
// 0 0 -1.84 0.17
// 1 0 0.22 0.44
// 2 0 0.20 0.45
// 3 0 0.16 0.47
// 4 0 1.33 0.18
// 0 0 -1.79 0.07
// 1 0 0.16 0.50
// 2 0 0.09 0.50
// 3 0 0.08 0.45
// 4 0 1.31 0.12
// 100 1 98.21 0.00 <-- alert is on, predicted spike
// 0 0 -13.83 0.29
// 1 0 -1.74 0.44
// 2 0 -0.47 0.46
// 3 0 -16.50 0.29
// 4 0 -29.82 0.21
}
private static void PrintPrediction(float value, SsaSpikePrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2]);
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
class SsaSpikePrediction
{
[VectorType(3)]
public double[] Prediction { get; set; }
}
}
}
Si applica a
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