public void Predict_HMMMultivariateAndFTSESeriesLength20_Predicted20Days()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var test = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 12, 18), new DateTime(2012, 01, 18));
var model = (HiddenMarkovModelMultivariateGaussianDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution>()
{
NumberOfStates = _NumberOfStates
});
model.Normalized = true;
model.Train(series, _NumberOfIterations, _LikelihoodTolerance);
var pred = new ViterbiBasedPredictor();
var request = new PredictionRequest {
TrainingSet = series, NumberOfDays = 20, TestSet = test
};
pred.NumberOfIterations = _NumberOfIterations;
pred.LikelihoodTolerance = _LikelihoodTolerance;
var predictions = pred.Predict(model, request);
Assert.AreEqual(20, predictions.Predicted.Length);
}
public void Evaluate_HMMMultivariateAndFTSESeriesLength20_ErrorEstimatorCalculated()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var test = util.GetSvcData(util.FTSEFilePath, new DateTime(2011, 12, 18), new DateTime(2012, 01, 18));
var model = (HiddenMarkovModelMultivariateGaussianDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <IMultivariateDistribution>()
{
NumberOfStates = _NumberOfStates
});
model.Normalized = true;
model.Train(series, _NumberOfIterations, _LikelihoodTolerance);
var pred = new ViterbiBasedPredictor();
var request = new PredictionRequest {
TrainingSet = series, NumberOfDays = 20, TestSet = test
};
pred.NumberOfIterations = _NumberOfIterations;
pred.LikelihoodTolerance = _LikelihoodTolerance;
var predictions = pred.Predict(model, request);
var errorRequest = new EvaluationRequest
{
EstimatorType = ErrorEstimatorType.Value,
PredictionParameters = request,
PredictionToEvaluate = predictions
};
var errorEstimation = pred.Evaluate(errorRequest);
for (int i = 0; i < series[0].Length; i++)
{
Assert.IsTrue(errorEstimation.CumulativeForecastError[i] > 0);
Assert.IsTrue(errorEstimation.MeanAbsoluteDeviation[i] > 0);
Assert.IsTrue(errorEstimation.MeanAbsolutePercentError[i] > 0);
Assert.IsTrue(errorEstimation.MeanError[i] > 0);
Assert.IsTrue(errorEstimation.MeanSquaredError[i] > 0);
Assert.IsTrue(errorEstimation.RootMeanSquaredError[i] > 0);
}
}
public void Predict_HMMMixtureAndFTSESeriesLength1_PredictedOneDay()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.FTSEFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfComponents = _NumberOfComponents, NumberOfStates = _NumberOfStates
});
model.Normalized = true;
model.Train(series, _NumberOfIterations, _LikelihoodTolerance);
var request = new PredictionRequest();
request.TrainingSet = series;
request.NumberOfDays = 1;
var pred = new ViterbiBasedPredictor();
var res = pred.Predict(model, request);
Assert.AreEqual(1, res.Predicted.Length);
}
