//-----------------------------------------------------------------------------------------------------
//Логарифмическое преобразование
public static RealMatrix[] GetLogTransforms(RealMatrix[] matrices, double coefficient)
{
LogTransform logTransform = new LogTransform(coefficient);
RealMatrix[] newMatrices = new RealMatrix[matrices.Length];
for (int index = 0; index < matrices.Length; index++)
{
RealMatrix matrix = matrices[index];
RealMatrix newMatrix = RealMatrixValuesTransform.TransformMatrixValues(matrix, logTransform);
newMatrices[index] = newMatrix;
}
return(newMatrices);
}
// this method fits an ARMA model to the data and returns it, in the process testing for non-stationarity and calculating the orders of the
// MA and AR terms of the model
public ARMAModel buildARMAModel(DataObject[] series, double siglevel, int len = 50, bool print = true, bool log = false)
{
bool nonstationarity = testStationarity(series, siglevel);
if (print)
{
Console.WriteLine("non-stationarity: " + nonstationarity.ToString());
}
int d = 0;
if (nonstationarity)
{
d = 1;
}
var tSeries = series;
//Difference the series
if (d != 0)
{
double[] dcol = new double[tSeries.Length];
for (int i = 0; i < dcol.Length; i++)
{
dcol[i] = (tSeries[i].Value);
}
double[] xdiff = ArrayManipulation.diff(dcol);
for (int i = dcol.Length - xdiff.Length; i < xdiff.Length; i++)
{
tSeries[i] = new DataObject(tSeries[i].Date, xdiff[i]);
}
}
var ts = new TimeSeries
{
Title = "Time Series",
Description = "TS Description"
};
for (int i = 0; i < series.GetLength(0); i++)
{
ts.Add(tSeries[i].Date, (tSeries[i].Value), false); //datetime, value, false
}
if (log)
{
//log transform the time series
var logTransform = new LogTransform();
logTransform.SetInput(0, ts, null);
logTransform.Recompute();
ts = logTransform.GetOutput(0) as TimeSeries;
}
//Use ACF and PACF to find ARMA parameters
var highInterval = 1.96 / Math.Sqrt(series.Length);
var acf = ts.ComputeACF(20, false);
int p = 1;
var low = acf[0];
for (int i = 0; i < acf.Count; i++)
{
if (Math.Min(low, acf[i]) <= highInterval && highInterval < Math.Max(acf[i], low))
{
p = i;
if (p != 0)
{
p--;
}
if (p == 0)
{
p = 1;
}
break;
}
low = acf[i];
}
var pacf = TimeSeries.GetPACFFrom(acf);
int q = 1;
low = pacf[0];
for (int i = 0; i < pacf.Count; i++)
{
if (Math.Min(low, pacf[i]) <= highInterval && highInterval < Math.Max(pacf[i], low))
{
q = i;
if (q != 0)
{
q--;
}
if (q == 0)
{
q = 1;
}
break;
}
low = pacf[i];
}
if (p > 4)
{
p = 4;
}
if (q > 4)
{
q = 4;
}
if (print)
{
Console.WriteLine("p: " + p.ToString() + " q: " + q.ToString());
Console.WriteLine("Fitting the model...");
}
var model = new ARMAModel(p, q);
model.TheData = ts;
Stopwatch watch = new Stopwatch();
watch.Start();
model.FitByMLE(200, 100, 0, null);
watch.Stop();
if (print)
{
Console.WriteLine("Model took " + watch.Elapsed.TotalSeconds.ToString() + " seconds to fit parameters to the data.");
Console.WriteLine("Model has been fitted.");
}
return(model);
}
