//once the time series data has been transformed under either an iterative or Weibull like transformation, the transformed
//data can be further standardised, the standardise() method of the Statistics class is used
//see Section 3.1 of accompanying manual
private void explrFormstandardisebt_Click(object sender, EventArgs e)
{
standardiseForm f1 = new standardiseForm();
f1.N1 = this.R;
f1.M1 = this.C;
//display the dialog
if (f1.ShowDialog() == DialogResult.OK)
{
//need to figure out the number of rows and number of columns in the underlying spreadsheet
int m2;
bool m2Test = int.TryParse(f1.standardisetxtbcols.Text, out m2);
int n2;
bool n2Test = int.TryParse(f1.standardisetxtbrows.Text, out n2);
if ((n2 > 0) && (m2 > 0) && (n2 <= R) && (m2 <= C))
{
P.standardise(P.TransformedData, this.R, this.C, n2, m2);
//now plot the means
explrFormMeans.Show();
explrFormMeans.Titles.Clear();
explrFormMeans.Titles.Add(new Title("Transformed Data: Mean of groups", Docking.Top, new Font("Verdana", 8f, FontStyle.Bold), Color.Black));
explrFormMeans.ChartAreas[0].AxisY.LabelStyle.Font = explrFormMeans.ChartAreas[0].AxisX.LabelStyle.Font = new Font("Arial", 11, GraphicsUnit.Pixel);
explrFormMeans.ChartAreas[0].RecalculateAxesScale();
explrFormMeans.Series.Clear();
var series10 = new System.Windows.Forms.DataVisualization.Charting.Series
{
Name = "Series10",
Color = System.Drawing.Color.Blue,
IsVisibleInLegend = false,
IsXValueIndexed = true,
ChartType = SeriesChartType.Line
};
this.explrFormMeans.Series.Add(series10);
explrFormMeans.ChartAreas[0].AxisY.Minimum = Math.Round(P.minimum(P.StdMeans) * 0.95, 2);
explrFormMeans.ChartAreas[0].AxisY.Maximum = Math.Round(P.maximum(P.StdMeans) * 1.05, 2);
int u = 0;
foreach (double dd in P.StdMeans)
{
series10.Points.AddXY(u, Math.Round(dd, 2));
u++;
}
//skewness and kurtosis for transformed data
standardisedlbl.Show();
standardisedskewlbl.Show();
standardisedkurtlbl.Show();
standardisedskewval.Show();
standardisedkurtval.Show();
double sk = Math.Round(P.skew(P.StandardisedTransformedData), 2);
double ku = Math.Round(P.kurt(P.StandardisedTransformedData), 2);
standardisedskewval.Text = sk.ToString();
standardisedkurtval.Text = ku.ToString();
}
}
}
//method which helps with differencing of the data
//the method also plots the acf and pacf of the differenced data
private void ARIMAdiff_Click(object sender, EventArgs e)
{
if (this.Mean > 0)
this.Mean = 0;
//form which will help to determine the order of differencing
standardiseForm f1 = new standardiseForm();
ARIMAresidacf.Hide();
ARIMAdgv.Hide();
ARIMAhist.Hide();
ARIMAparameterdgv.Hide();
ARIMAparameterdgv.Hide();
ARIMAplbl.Hide();
ARIMArlbl.Hide();
ARIMAresidqqplot.Hide();
label1.Hide();
ARIMAroutput.Hide();
ARIMAsquaredresiduals.Hide();
ARIMAubt.Hide();
ARIMAsave.Hide();
//we don't need all the info from the "standardiseForm" hence we can hide some of objects in the form
f1.standardisetxtbcols.Text = "1";
f1.standardisetxtbcols.Visible = false;
f1.standardiselblcols.Visible = false;
f1.standardiselblh.Text = "Provide an Integer to specify the differencing:";
f1.standardiselblrows.Text = "Integer";
f1.N1 = 100;
f1.M1 = 100;
//display the dialog
if (f1.ShowDialog() == DialogResult.OK)
{
int m2;
bool m2Test = int.TryParse(f1.standardisetxtbcols.Text, out m2);
int n2;
bool n2Test = int.TryParse(f1.standardisetxtbrows.Text, out n2);
if ((n2 > 0))
{
//display the chart
ARIMAac2.Show();
this.P.DifferencedData.Clear();
this.P.Diff = n2;
//now difference the data
P.difference();
//calculate the autcorrelation and partial autocorrelation of the data
List<double> sc = P.sampleautocorrelation(P.DifferencedData, 50);
sc.RemoveAt(0);
List<double> spc = P.samplepartialautocorrelation(P.DifferencedData, 50);
spc.RemoveAt(0);
//we will use the following code to create the horizontal lines in the ACF and PACF plots
int n = this.P.DifferencedData.Count();
double l1 = 1.96 / Math.Sqrt(n);
double l2 = -1.96 / Math.Sqrt(n);
List<double> line1 = new List<double>();
List<double> line2 = new List<double>();
for (int i = 0; i < sc.Count(); i++)
{
line1.Add(l1);
line2.Add(l2);
}
//plot of the autcoorrelation
ARIMAac2.Titles.Clear();
ARIMAac2.Titles.Add(new Title("Differenced Data: ACF and PACF", Docking.Top, new Font("Verdana", 8f, FontStyle.Bold), Color.Black));
ARIMAac2.ChartAreas[0].AxisY.LabelStyle.Font = ARIMAac2.ChartAreas[0].AxisX.LabelStyle.Font = new Font("Arial", 11, GraphicsUnit.Pixel);
ARIMAac2.ChartAreas[0].RecalculateAxesScale();
ARIMAac2.Series.Clear();
var series3 = new System.Windows.Forms.DataVisualization.Charting.Series
{
Name = "ACF",
Color = System.Drawing.Color.Blue,
IsVisibleInLegend = true,
IsXValueIndexed = true,
ChartType = SeriesChartType.Column
};
this.ARIMAac2.Series.Add(series3);
int z = 0;
foreach (double dd in sc)
{
series3.Points.AddXY(z + 1, sc[z]);
z++;
}
var series4 = new System.Windows.Forms.DataVisualization.Charting.Series
{
Name = "PACF",
Color = System.Drawing.Color.Red,
IsVisibleInLegend = true,
IsXValueIndexed = true,
ChartType = SeriesChartType.Column
};
this.ARIMAac2.Series.Add(series4);
z = 0;
foreach (double dd in spc)
{
series4.Points.AddXY(z + 1, spc[z]);
z++;
}
var series23 = new System.Windows.Forms.DataVisualization.Charting.Series
{
Name = "Series23",
Color = System.Drawing.Color.Black,
IsVisibleInLegend = false,
IsXValueIndexed = true,
ChartType = SeriesChartType.Line
};
this.ARIMAac2.Series.Add(series23);
z = 0;
foreach (double dd in line1)
{
series23.Points.AddXY(z + 1, line1[z]);
z++;
}
var series24 = new System.Windows.Forms.DataVisualization.Charting.Series
{
Name = "Series24",
Color = System.Drawing.Color.Black,
IsVisibleInLegend = false,
IsXValueIndexed = true,
ChartType = SeriesChartType.Line
};
this.ARIMAac2.Series.Add(series24);
z = 0;
foreach (double dd in line2)
{
series24.Points.AddXY(z + 1, line2[z]);
z++;
}
ARIMAac2.Invalidate();
ARIMAac2.ChartAreas[0].AxisY.Maximum = Math.Max(P.maximum(sc), P.maximum(spc));
}
else
{
//ensure that the Diff is set to 0 as the user has decided not to use differencing
this.P.Diff = 0;
}
}
//end of method
}
