public void SSAForecast_Test()
{
var _ts = getAirPassengersData(); // Monthly time-series data
//DataFrame.ToCsv("usa_deaths.csv",df);
//var ts = df.ToSeries().Select(x => Convert.ToDouble(x));
//embed
var ssa = new SSA(_ts);
//embed
var x = ssa.Embedding(12);
//decompose
ssa.Decompose();
//
var charts = ssa.PlotEigenPairs();
XPlot.Plotly.Chart.ShowAll(charts);
var ff = ssa.Forecast(new int[] { 1, 2, 3, 5, 7 }, 6, method: Forecasting.Rforecasing);
var scatters1 = new Scatter()
{
name = "Predicted", x = Enumerable.Range(1, ff.Length), y = ff, mode = "line",
};
var scatters2 = new Scatter()
{
name = "Actual", x = Enumerable.Range(1, _ts.Count()), y = _ts, mode = "line", fillcolor = "Blue"
};
var chart = XPlot.Plotly.Chart.Plot <Trace>(new Trace[] { scatters1, scatters2 });
chart.Show();
}
public void SSA_TestVForecast()
{
int stepsAhead = 11;
var ssa = new SSA(_ts);
ssa.Embedding(10);
ssa.Decompose();
//r forecasting
var ts1 = ssa.Forecast(new int[2] {
1, 2
}, stepsAhead, method: Forecasting.Vforecasting);
Assert.Equal(1, ts1[0], 5);
Assert.Equal(2, ts1[1], 5);
Assert.Equal(3, ts1[2], 5);
Assert.Equal(4, ts1[3], 5);
Assert.Equal(5, ts1[4], 5);
Assert.Equal(6, ts1[5], 5);
Assert.Equal(7, ts1[6], 5);
Assert.Equal(8, ts1[7], 5);
Assert.Equal(9, ts1[8], 5);
Assert.Equal(10, ts1[9], 5);
Assert.Equal(11, ts1[10], 5);
Assert.Equal(12, ts1[11], 5);
Assert.Equal(13, ts1[12], 5);
Assert.Equal(14, ts1[13], 5);
Assert.Equal(15, ts1[14], 5);
Assert.Equal(16, ts1[15], 5);
Assert.Equal(17, ts1[16], 5);
Assert.Equal(18, ts1[17], 5);
Assert.Equal(19, ts1[18], 5);
Assert.Equal(20, ts1[19], 5);
//forecast
Assert.Equal(21, ts1[20], 5);
Assert.Equal(22, ts1[21], 5);
Assert.Equal(23, ts1[22], 5);
Assert.Equal(24, ts1[23], 5);
Assert.Equal(25, ts1[24], 5);
Assert.Equal(26, ts1[25], 5);
Assert.Equal(27, ts1[26], 5);
Assert.Equal(28, ts1[27], 5);
Assert.Equal(29, ts1[28], 5);
Assert.Equal(30, ts1[29], 5);
Assert.Equal(31, ts1[30], 5);
}
public static void Forecasting()
{
//
var strPath = $"AirPassengers.csv";
var mlDF = DataFrame.FromCsv(strPath, sep: ',');
var ts = mlDF["#Passengers"].Select(f => Convert.ToDouble(f));//create time series
//create Singular Spectrum Analysis object
var ssa = new SSA(ts);
//perform analysis
ssa.Fit(36);
//helper vars to hold data
int n = 4;
var setIndex = Enumerable.Range(0, n);
double[][] ts_components = new double[setIndex.Count()][];
//cumulative components array
double[] cumTS = new double[ts.Count()];
//take the first n components and plot it
var models = new List <PlotModel>();
foreach (var i in setIndex)
{
ts_components[i] = ssa.Reconstruct(ssa.Xs[i]);
cumTS = cumTS.Add(ts_components[i]);
var m11 = Daany.Plot.ChartComponent.LinePlot("", $"TS Components {i+1}",
Enumerable.Range(1, ts_components[i].Length).Select(t => (double)t).ToArray(), ts_components[i], System.Drawing.Color.Blue, OxyPlot.MarkerType.Plus);
//
models.Add(m11);
}
//plot component together
ChartComponent.ShowPlots("Passenger TS Components", models.ToArray()).Wait();
//plot sum of the components
var x = Enumerable.Range(1, cumTS.Length).Select(t => (double)t).ToArray();
var m1 = Daany.Plot.ChartComponent.LineSeries("Predicted SSA",
x, cumTS, System.Drawing.Color.Blue, OxyPlot.MarkerType.Plus);
var m2 = Daany.Plot.ChartComponent.LineSeries("Original Time Series",
x, ts.ToArray(), System.Drawing.Color.Red, OxyPlot.MarkerType.Plus);
var model = new PlotModel();
model.Series.Add(m1);
model.Series.Add(m2);
ChartComponent.ShowPlots("Passenger TS vs Predicted", model).Wait();
//Forecasting of time series
var stepAHead = 12;
var predictedTS = ssa.Reconstruct(15);
var predicted = ssa.Forecast(stepAHead);
//create three plotmodel: actual TS, predicted TS, Forcast ts ahead
var mm3 = Daany.Plot.ChartComponent.LineSeries("Predicted Time Series",
x, predictedTS, System.Drawing.Color.Green, OxyPlot.MarkerType.Plus);
var mm2 = Daany.Plot.ChartComponent.LineSeries("Original Time Series",
x, ts.ToArray(), System.Drawing.Color.Red, OxyPlot.MarkerType.Plus);
//forecast steps ahead
var x1 = Enumerable.Range(cumTS.Length, stepAHead + 1).Select(t => (double)t).ToArray();
//start with the last point form the times series
var y = predicted.Skip(ts.Count() - 1).Take(stepAHead + 1).ToArray();
var mm1 = Daany.Plot.ChartComponent.LineSeries($"Forecast of {stepAHead}",
x1, y, System.Drawing.Color.Blue, OxyPlot.MarkerType.Plus);
//add series to model
var model1 = new PlotModel();
model1.Series.Add(mm1);
model1.Series.Add(mm2);
model1.Series.Add(mm3);
//plot forecasting
ChartComponent.ShowPlots("Passenger TS vs Forecasting", model1).Wait();
}
public void SSA_Correlation_Test02()
{
//
var df = getMonthlyDeatsData(); // Monthly time-series data
//DataFrame.ToCsv("usa_deaths.csv",df);
var ts = df.ToSeries().Select(x => Convert.ToDouble(x));
//embed
var ssa = new SSA(ts);
//embed
var x = ssa.Embedding(24);
PlotMatrix(x).Show();
//decompose
ssa.Decompose();
var plt = new List <XPlot.Plotly.PlotlyChart> ();
foreach (var tm in ssa.EM)
{
plt.Add(PlotMatrix(tm.Value));
}
XPlot.Plotly.Chart.ShowAll(plt);
var f1 = ssa.Reconstruct(1);
var sct1 = new Scatter()
{
name = "Actual", x = Enumerable.Range(1, f1.Length), y = f1, mode = "line",
};
var chart1 = XPlot.Plotly.Chart.Plot <Trace>(new Trace[] { sct1 });
chart1.Show();
var ff = ssa.Forecast(new int[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 }, 6, method: Forecasting.Rforecasing);
var scatters1 = new Scatter()
{
name = "Predicted", x = Enumerable.Range(1, ff.Length), y = ff, mode = "line",
};
var scatters2 = new Scatter()
{
name = "Actual", x = Enumerable.Range(1, ts.Count()), y = ts, mode = "line", fillcolor = "Blue"
};
var chart = XPlot.Plotly.Chart.Plot <Trace>(new Trace[] { scatters1, scatters2 });
// chart.Show();
// int i= 0;
// ssa.PlotComponents(12);
//
ssa.PlotSingularValues().Show();
//
var charts = ssa.PlotEigenPairs();
XPlot.Plotly.Chart.ShowAll(charts);
var w = ssa.WCorrelation(new int[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 });
ssa.PlotwCorrelation(w).Show();
}