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public static void DetectSpikeBySsa(IReadOnlyList data
, double confidence
, int pvalueHistoryLength
, int trainingWindowSize
, int seasonalityWindowSize)
{
var context = new MLContext();
var dataView = context.Data.LoadFromEnumerable(data);
var pipeline = context.Transforms.DetectSpikeBySsa(
outputColumnName: nameof(AnomalyPrediction.Prediction)
, inputColumnName: nameof(SensorData.Value)
, confidence: confidence // 99.0
, pvalueHistoryLength: pvalueHistoryLength // data.Count / 2
, trainingWindowSize: trainingWindowSize // data.Count
, seasonalityWindowSize: seasonalityWindowSize // data.Count / 4);

var model = pipeline.Fit(dataView);
var transformedData = model.Transform(dataView);

List results = [];
var predictions = context.Data.CreateEnumerable(transformedData, reuseRowObject: false).ToList();
for (int i = 0; i < predictions.Count; i++)
{
var sensorData = data[i];
var prediction = predictions[i];
var isAnomaly = prediction.Prediction[0] is 1;
Console.WriteLine($"{prediction.Prediction[0]}\t {prediction.Prediction[1]:f2} \t {prediction.Prediction[2]:f2}");
results.Add(new AnomalyDetectionResult
{
IsAnomaly = isAnomaly,
Value = sensorData.Value,
Speed = sensorData.Speed,
Timestamp = sensorData.Timestamp
});

if (!isAnomaly)
{
// Todo
}
}
}