public override TimeSeries BuildForecasts(TimeSeries simulatedData, List <DateTime> futureTimes)
{
// fit model first, using maximum likelihood estimation
var model = new ARMAModel(mAROrder, mMAOrder); // create the model object
model.TheData = simulatedData; // this is the data we want to fit the model to
model.FitByMLE(mNumberIterLDS, mNumberIterOpt, 0, null); // first param is # low discrepancy sequence iterations, should be at least about 200
// second param is # standard optimizer iterations, should be at least about 100
// now do some forecasting beyond the end of the data
var forecaster = new ForecastTransform();
forecaster.FutureTimes = futureTimes.ToArray(); // these are future times at which we want forecasts
// for now, ARMA models do not use the times in any meaningful way when forecasting,
// they just assume that these are the timestamps for the next sequential points
// after the end of the existing time series
forecaster.SetInput(0, model, null); // the ARMA model used for forecasting
forecaster.SetInput(1, simulatedData, null); // the original data
// normally you would call the Recompute() method of a transform, but there is no need
// here; it is automatically called as soon as all inputs are set to valid values (in the 2 statements above)
var predictors = forecaster.GetOutput(0) as TimeSeries;
return(predictors);
}
public void ArmaFromExtData()
{
var arma = new ARMAModel(1, 1, _extData);
Assert.IsTrue(arma.CanUseMLE());
arma.FitByMLE(200, 100, 0, null);
Write($"Sample mean: {_extData.SampleMean()}, From model: {arma.Mu} \n");
Write($"Desc: {arma.Description}");
}
public void ArmaSimulates()
{
var _arma = new ARMAModel(1, 1);
var simulateData = _arma.SimulateData(_dates, 22);
Assert.NotNull(simulateData);
Assert.IsTrue(simulateData.Count > 0);
Write($"Sample mean: {simulateData.SampleMean()}");
_arma.TheData = simulateData; // Feed back in
_arma.FitByMLE(200, 100, 0, null);
Write(_arma.Description);
}
public EstimationResult Estimate(IEnumerable <IDateValue> dateValues)
{
var series = new TimeSeries();
dateValues.ForEach(x => series.Add(x.Date, x.Value, true));
armaModel.SetInput(0, series, null);
armaModel.FitByMLE(200, 100, 0, null);
armaModel.ComputeResidualsAndOutputs();
var result = armaModel.GetOutput(3) as TimeSeries;
return(EstimationResult.Create(result[0], this));
}
protected override TimeSeries _BuildOutput(TimeSeries simulatedData, object userState = null)
{
ARMAModel model = new ARMAModel(mAROrder, mMAOrder);
model.SetInput(0, simulatedData, null);
//Maximum Likelihood Estimation
model.FitByMLE(mNumberIterLDS, mNumberIterOpt, 0, null); // first param is # low discrepancy sequence iterations, should be at least about 200
// second param is # standard optimizer iterations, should be at least about 100
//Compute the residuals
model.ComputeResidualsAndOutputs();
//model1.GetOutputName(3);
//model1.Description;
//Get the predicted values
return(model.GetOutput(3) as TimeSeries);
}
// 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);
}
