public void ChangePointDetectionWithSeasonality()
{
using (var env = new ConsoleEnvironment(conc: 1))
{
const int ChangeHistorySize = 2000;
const int SeasonalitySize = 1000;
const int NumberOfSeasonsInTraining = 5;
const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize;
List <Data> data = new List <Data>();
var dataView = env.CreateStreamingDataView(data);
var args = new SsaChangePointDetector.Arguments()
{
Confidence = 95,
Source = "Value",
Name = "Change",
ChangeHistoryLength = ChangeHistorySize,
Data = dataView,
TrainingWindowSize = MaxTrainingSize,
SeasonalWindowSize = SeasonalitySize
};
for (int j = 0; j < NumberOfSeasonsInTraining; j++)
{
for (int i = 0; i < SeasonalitySize; i++)
{
data.Add(new Data(i));
}
}
for (int i = 0; i < ChangeHistorySize; i++)
{
data.Add(new Data(i * 100));
}
var detector = TimeSeriesProcessing.SsaChangePointDetector(env, args);
var output = detector.Model.Apply(env, dataView);
var enumerator = output.AsEnumerable <Prediction>(env, true).GetEnumerator();
Prediction row = null;
List <double> expectedValues = new List <double>()
{
0, 0, 0.5, 0, 0, 1, 0.15865526383236372,
0, 0, 1.6069464981555939, 0.05652458872960725, 0, 0, 2.0183047652244568, 0.11021633531076747, 0
};
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.Equal(expectedValues[index++], row.Change[0], precision: 7);
Assert.Equal(expectedValues[index++], row.Change[1], precision: 7);
Assert.Equal(expectedValues[index++], row.Change[2], precision: 7);
Assert.Equal(expectedValues[index++], row.Change[3], precision: 7);
}
}
}
public void ChangeDetection()
{
using (var env = new ConsoleEnvironment(conc: 1))
{
const int size = 10;
List <Data> data = new List <Data>(size);
var dataView = env.CreateStreamingDataView(data);
List <Data> tempData = new List <Data>();
for (int i = 0; i < size / 2; i++)
{
tempData.Add(new Data(5));
}
for (int i = 0; i < size / 2; i++)
{
tempData.Add(new Data((float)(5 + i * 1.1)));
}
foreach (var d in tempData)
{
data.Add(new Data(d.Value));
}
var args = new IidChangePointDetector.Arguments()
{
Confidence = 80,
Source = "Value",
Name = "Change",
ChangeHistoryLength = size,
Data = dataView
};
var detector = TimeSeriesProcessing.IidChangePointDetector(env, args);
var output = detector.Model.Apply(env, dataView);
var enumerator = output.AsEnumerable <Prediction>(env, true).GetEnumerator();
Prediction row = null;
List <double> expectedValues = new List <double>()
{
0, 5, 0.5, 5.1200000000000114E-08, 0, 5, 0.4999999995, 5.1200000046080209E-08, 0, 5, 0.4999999995, 5.1200000092160303E-08,
0, 5, 0.4999999995, 5.12000001382404E-08
};
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.Equal(expectedValues[index++], row.Change[0]);
Assert.Equal(expectedValues[index++], row.Change[1]);
Assert.Equal(expectedValues[index++], row.Change[2]);
Assert.Equal(expectedValues[index++], row.Change[3]);
}
}
}