public void LinearPredictor1Indicator()
{
TimeSeries indicator = TimeSeries.CreateDailySinusoidalTimeSeries(1, 2, 0, new DateTime(2000, 1, 1), new DateTime(2000, 12, 31));
indicator.Name = "INDICATOR";
TimeSeries target = indicator.OffsetBy(new TimeSpan(10, 0, 0, 0));
target.Name = "TARGET";
CorrelationAnalysisParameters parameters = new CorrelationAnalysisParameters()
{
Span = new TimeSpan(1, 0, 0, 0),
NumberOfSpansInthePast = 10,
NumberOfSpansIntheFuture = -10,
};
IEnumerable <TemporalGapTuple> analysis = TimeSeriesCorrelation.DoCorrelationAnalysis(indicator, target, parameters);
TemporalGapGroup g = new TemporalGapGroup()
{
Pairs = analysis
.Where(a => Math.Abs(a.Correlation - 1) < tolerance)
.ToList(),
};
TimeSpan predictionSpan = new TimeSpan(5, 0, 0, 0);
DataTable table = g.GetPredictionTable(target.Name, predictionSpan);
LinearPredictor linearPredictor = new LinearPredictor();
linearPredictor.Learn(
table: table,
trainingLabel: indicator.Name,
targetLabel: target.Name);
TimeSpan correlationSpan = new TimeSpan(10, 0, 0, 0);
Assert.IsTrue(target.Dates
.Where(day => target.ContainsValueAt(day.Add(predictionSpan)) &&
indicator.ContainsValueAt(day.Add(-correlationSpan)) &&
indicator.ContainsValueAt(day.Add(-correlationSpan).Add(predictionSpan)))
.All(day =>
{
double incrementIndicator = indicator[day.Add(-correlationSpan).Add(predictionSpan)] - indicator[day.Add(-correlationSpan)];
DataRow row = table.NewRow();
row[indicator.Name] = incrementIndicator;
return(Math.Abs((double)linearPredictor.Predict(row) - incrementIndicator) < tolerance);
}));
}
public void LinearPredictor2Indicators()
{
DateTime firstDate = new DateTime(2000, 1, 1);
DateTime lastDate = new DateTime(2000, 12, 31);
TimeSeries ind1 = TimeSeries.CreateDailyLinearTimeSeries(0, 100, firstDate, lastDate);
ind1.Name = "ind1";
TimeSeries ind2 = TimeSeries.CreateDailySinusoidalTimeSeries(1, 2 * Math.PI / 365, 0, firstDate, lastDate);
ind2.Name = "ind2";
List <TimeSeries> indicators = new List <TimeSeries>()
{
ind1, ind2
};
TimeSpan predictionSpan = new TimeSpan(5, 0, 0, 0);
TimeSpan correlationSpan = new TimeSpan(10, 0, 0, 0);
TimeSeries target = ind1
.Sum(ind2.MultiplyBy(2))
.OffsetBy(correlationSpan);
target.Name = "target";
TemporalGapGroup g = new TemporalGapGroup()
{
Pairs = new List <TemporalGapTuple>()
{
new TemporalGapTuple()
{
Indicator = ind1,
Target = target,
TemporalGap = correlationSpan,
},
new TemporalGapTuple()
{
Indicator = ind2,
Target = target,
TemporalGap = correlationSpan,
},
}
};
DataTable table = g.GetPredictionTable(target.Name, predictionSpan);
LinearPredictor linearPredictor = new LinearPredictor()
{
UseIntercept = false
};
linearPredictor.Learn(
table: table,
trainingLabels: new List <string>()
{
ind1.Name, ind2.Name
},
targetLabel: target.Name);
Assert.IsTrue(target.Dates
.Where(day => target.ContainsValueAt(day.Add(predictionSpan)) &&
indicators.All(ind => ind.ContainsValueAt(day.Add(-correlationSpan)) &&
ind.ContainsValueAt(day.Add(-correlationSpan).Add(predictionSpan))))
.All(day =>
{
DataRow row = table.NewRow();
indicators
.ForEach(ind => row[ind.Name] = ind[day.Add(-correlationSpan).Add(predictionSpan)] - ind[day.Add(-correlationSpan)]);
double predictedTargetIncrement = (double)linearPredictor.Predict(row);
double realTargetIncrement = target[day.Add(predictionSpan)] - target[day];
return(Math.Abs(predictedTargetIncrement - realTargetIncrement) < tolerance);
}));
}
