/// <summary>
/// Predicts next value for the time series.
/// The prediction is based on the distance of last day observation from the observation
/// with closest likelihood.
/// </summary>
/// <param name="observations">Observation matrix. As a static database of observations</param>
/// <param name="weights">Observation weights</param>
/// <returns>Predicted vector</returns>
public IPredictionResult Predict(double[][] observations, double[] weights)
{
var model = new GaussianMixtureModel(_mixture.Coefficients, _mixture.Components);
var request = new PredictionRequest();
request.TrainingSet = observations;
request.NumberOfDays = 1;
request.Tolerance = PREDICTION_LIKELIHOOD_TOLERANCE;
var predictor = new LikelihoodBasedPredictor();
return(predictor.Predict(model, request));
}
public IEvaluationResult EvaluatePrediction(IPredictionResult predicted, double[][] observed)
{
var request = new EvaluationRequest();
request.PredictionParameters = new PredictionRequest();
request.PredictionToEvaluate = new PredictionResult();
request.EstimatorType = ErrorEstimatorType.Value;
request.PredictionParameters.Tolerance = PREDICTION_LIKELIHOOD_TOLERANCE;
request.PredictionParameters.TestSet = observed;
request.PredictionToEvaluate.Predicted = predicted.Predicted;
var predictor = new LikelihoodBasedPredictor();
return(predictor.Evaluate(request));
}
