public void SsaForecast()
{
var env = new MLContext(1);
const int changeHistorySize = 10;
const int seasonalitySize = 10;
const int numberOfSeasonsInTraining = 5;
List <Data> data = new List <Data>();
var dataView = env.Data.LoadFromEnumerable(data);
var args = new SsaForecastingTransformer.Options()
{
ConfidenceLevel = 0.95f,
Source = "Value",
Name = "Forecast",
ConfidenceLowerBoundColumn = "MinCnf",
ConfidenceUpperBoundColumn = "MaxCnf",
WindowSize = 10,
SeriesLength = 11,
TrainSize = 22,
Horizon = 4,
IsAdaptive = true
};
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));
}
// Train
var detector = new SsaForecastingEstimator(env, args).Fit(dataView);
// Transform
var output = detector.Transform(dataView);
// Get predictions
var enumerator = env.Data.CreateEnumerable <ForecastPrediction>(output, true).GetEnumerator();
ForecastPrediction row = null;
List <float> expectedForecast = new List <float>()
{
0.191491723f, 2.53994083f, 5.26454258f, 7.37313938f
};
List <float> minCnf = new List <float>()
{
-3.9741993f, -2.36872721f, 0.09407653f, 2.18899345f
};
List <float> maxCnf = new List <float>()
{
4.3571825f, 7.448609f, 10.435009f, 12.5572853f
};
enumerator.MoveNext();
row = enumerator.Current;
for (int localIndex = 0; localIndex < 4; localIndex++)
{
Assert.Equal(expectedForecast[localIndex], row.Forecast[localIndex], precision: 7);
Assert.Equal(minCnf[localIndex], row.MinCnf[localIndex], precision: 7);
Assert.Equal(maxCnf[localIndex], row.MaxCnf[localIndex], precision: 7);
}
}
