//this method is called when the user clicks the Forecast Run button
//depending on the user input it a) creates the forecasts b) creates the simulations c) calls the plot1() plot2() and plot(3) methods
private void ForecastRun_Click(object sender, EventArgs e)
{
System.Windows.Forms.Cursor old = this.Cursor;
this.Cursor = Cursors.AppStarting;
//getting all the information we need about the model
int mposition = 0;
mposition = Mdgv.SelectedRows[0].Index;
int nar = 0;
int nma = 0;
int nd = 0;
int narchp = 0;
int narchq = 0;
bool subtmean = false;
string transform;
double shape;
List <double> armaestimates = new List <double>();
List <double> garchestimates = new List <double>();
double v = 0;
nar = Modellist[mposition].P;
nma = Modellist[mposition].Q;
nd = Modellist[mposition].D;
subtmean = Modellist[mposition].Subtractmean;
v = Modellist[mposition].Variance;
narchp = Modellist[mposition].archP;
narchq = Modellist[mposition].archQ;
for (int i = 0; i < nar + nma; i++)
{
armaestimates.Add(Modellist[mposition].Parameters[i]);
}
for (int i = 0; i < narchp + narchq + 1; i++)
{
garchestimates.Add(Modellist[mposition].archParameters[i]);
}
transform = Modellist[mposition].Transformtype;
shape = Modellist[mposition].Transformshape;
//temporary lists used in plot2
List <double> p2listf = new List <double>();
List <double> p2lista = new List <double>();
List <List <double> > p3simulations = new List <List <double> >();
List <List <double> > p3lists = new List <List <double> >();
int simulations = 100;
//Remember:
//TSeries will contain the data - if the model transform is either Iterative or Weibull it will be transformed, otherwise it will be the unadjusted data
//Data will contain the info for the first graph i.e. the object on which the models were calibrated. This could be the original data, transformed data, transformed data - mean, differenced data
//setup the variables for the forecast
int nsteps = (int)this.numericUpDown1.Value;
int point = (int)this.numericUpDown2.Value;
//now do the forecast for the first graph
List <double> forecast = P.predictnstepsahead(data, armaestimates, nar, nma, nsteps, point);
List <double> actual = new List <double>();
int counter = 0;
while ((point - 1 + counter < data.Count()) && (counter <= nsteps))
{
actual.Add(this.Data[point - 1 + counter]);
counter++;
}
//now draw the first plot
plot1(forecast, actual, nsteps, point);
//if the time series is differenced then we need to calculate the forecast and actual values for the original series
//and then undo the transform
List <double> forecastD = new List <double>();
List <double> actualD = new List <double>();
if (nd > 0)
{
//Note if we assume d = 1, then we need to start with "point+1" for the forecasts in plot 1 and plot 2 to commence at the exact
//same time point. This is due to differencing and the inclusion of a 0 in the predictnstepsaheadDD function for convention.
//Hence, if d = 2 or larger, then the time points on the two graphs may not coincide exactly.
//creating the forecast if we have an ARIMA model
List <double> temp = P.predictnstepsaheadDD(Tseries, armaestimates, nar, nma, nsteps, point + 1, nd);
foreach (double dd in temp)
{
forecastD.Add(dd);
}
//creating a list to hold the actual data if we have an ARIMA model
counter = 0;
while ((point + counter < Tseries.Count()) && (counter <= nsteps))
{
actualD.Add(this.Tseries[point + counter]);
counter++;
}
}
if ((transform == "Iterative") || (transform == "Weibull") || (subtmean == true))
{
//mean
double m = 0;
if (subtmean == true)
{
m = P.avg(Tseries);
}
//if i in the ARIMA model is >0 then we want to work with forecastD and actualD lists
if (nd > 0)
{
List <double> tempA = P.unwind(forecastD, transform, shape, m);
List <double> tempB = P.unwind(actualD, transform, shape, m);
foreach (double dd in tempA)
{
p2listf.Add(dd);
}
foreach (double dd in tempB)
{
p2lista.Add(dd);
}
}
else
{
List <double> tempA = P.unwind(forecast, transform, shape, m);
List <double> tempB = P.unwind(actual, transform, shape, m);
foreach (double dd in tempA)
{
p2listf.Add(dd);
}
foreach (double dd in tempB)
{
p2lista.Add(dd);
}
}
plot2(p2listf, p2lista, nsteps, point);
}
//ARMAsimulations with nd = 0
if ((nd == 0) && (narchq == 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.ARMAsimulatenstepsahead(data, armaestimates, nar, nma, nsteps, point, v);
p3simulations.Add(tempC);
}
}
//ARCHsimulations with nd = 0
if ((nd == 0) && (narchq > 0) && (narchp == 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.ARCHsimulatenstepsahead(data, armaestimates, nar, nma, nsteps, point, v, narchq, garchestimates);
p3simulations.Add(tempC);
}
}
//GARCHsimulations with nd = 0
if ((nd == 0) && (narchq > 0) && (narchp > 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.GARCHsimulatenstepsahead(data, armaestimates, nar, nma, nsteps, point, v, narchq, narchp, garchestimates);
p3simulations.Add(tempC);
}
}
//ARMAsimulations with nd > 0
if ((nd > 0) && (narchq == 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.ARMAsimulatenstepsaheadDD(Tseries, armaestimates, nar, nma, nsteps, point, v, nd);
p3simulations.Add(tempC);
}
}
//ARCHsimulations with nd > 0
if ((nd > 0) && (narchq > 0) && (narchp == 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.ARCHsimulatenstepsaheadDD(Tseries, armaestimates, nar, nma, nsteps, point, v, nd, narchq, garchestimates);
p3simulations.Add(tempC);
}
}
//GARCHsimulations with nd > 0
if ((nd > 0) && (narchq > 0) && (narchp > 0))
{
for (int i = 0; i < simulations; i++)
{
List <double> tempC = P.GARCHsimulatenstepsaheadDD(Tseries, armaestimates, nar, nma, nsteps, point, v, nd, narchq, narchp, garchestimates);
p3simulations.Add(tempC);
}
}
//now we need to check if we need to do any unwinding
if ((transform == "Iterative") || (transform == "Weibull") || (subtmean == true))
{
//mean
double m = 0;
if (subtmean == true)
{
m = P.avg(Tseries);
}
for (int i = 0; i < simulations; i++)
{
List <double> tempD = P.unwind(p3simulations[i], transform, shape, m);
p3lists.Add(tempD);
}
plot3(p3lists, nsteps, point);
}
this.Cursor = old;
}
//on selecting one of the values from the listbox, this method is called.
//It a)populates the Tseries list b)calls the loadmodels() method c) calls the populatedatagridview() method
private void ARCHlb_SelectedValueChanged(object sender, EventArgs e)
{
System.Windows.Forms.Cursor old = this.Cursor;
this.Cursor = Cursors.AppStarting;
Tseries.Clear();
int position = ARCHlb.SelectedIndex;
if (position == -1)
{
this.Cursor = old;
return;
}
string curItem = Tab[position];
string df = Flocations[position];
string f = ";Extended Properties=\"Excel 12.0;HDR=NO\"";
string c = "Provider=Microsoft.ACE.OLEDB.12.0;Data Source=" + df + f;
string interim1 = Fnames[position].Replace(".xlsx", "");
string interim2 = Tab[position].Replace("$", "");
string interim3 = Flocations[position].Replace(fnames[position], interim1 + interim2 + ".csv");
int counter = 0;
string line1;
string line2;
string line3;
double t = 0;
double tscale = 0;
try
{
StreamReader sr = new StreamReader(interim3);
while (counter < 1)
{
line1 = sr.ReadLine();
line2 = sr.ReadLine();
line3 = sr.ReadLine();
bool tf = Double.TryParse(line2, out t);
bool tfscale = Double.TryParse(line3, out tscale);
if (line1 == "Iterative")
{
this.Type = "Iterative";
this.Transform = t;
this.Transformscale = 0;
}
if (line1 == "Weibull")
{
this.Type = "Weibull";
this.Transform = t;
this.Transformscale = tscale;
}
counter++;
}
}
catch
{
}
DataSet data = new DataSet();
OleDbConnection con = new OleDbConnection(c);
DataTable dataTable = new DataTable();
string query = string.Format("SELECT * FROM [{0}]", curItem);
OleDbDataAdapter adapter = new OleDbDataAdapter(query, con);
try
{
con.Open();
adapter.Fill(dataTable);
data.Tables.Add(dataTable);
int m = 0, n = 0;
m = data.Tables[0].Rows.Count;
n = dataTable.Columns.Count;
this.R = m;
this.C = n;
int q = 0, r = 0;
try
{
for (q = 0; q < m; q++)
{
for (r = 0; r < n; r++)
{
double d = (double)data.Tables[0].Rows[q].ItemArray[r];
Tseries.Add(d);
}
}
}
catch (Exception x)
{
MessageBox.Show("No data in the underlying tab. Error:" + x.Message);
}
}
catch (Exception ex)
{
MessageBox.Show("The listbox selection did not work. Error: " + ex.Message);
}
finally
{
con.Close();
Modellist.Clear();
this.loadmodels(position);
if (Modellist.Count() == 0)
{
hide();
string message = "No ARIMA models relating to this data set have been found.\nPlease go back to ARIMA section, run the models and save output.";
string caption = "ARIMA Models";
MessageBoxButtons buttons = MessageBoxButtons.OK;
DialogResult result;
result = MessageBox.Show(message, caption, buttons);
}
else
{
populatedatagridview();
this.P = new Statistics(Tseries);
}
this.Cursor = old;
}
}
//this method is called once the user chooses a tab from the listbox
//it (a) loads the time series data (b) calls the Hookup method which will produce a number of graphs and summary statistics c) saves a copy of the underlying data to a CSV file
private void explrlb_SelectedValueChanged(object sender, EventArgs e)
{
System.Windows.Forms.Cursor old = this.Cursor;
this.Cursor = Cursors.AppStarting;
//clear any data that may already be in the Tseries object
Tseries.Clear();
int position = explrlb.SelectedIndex;
//protects in the event that the user selectes a blank line
if (position == -1)
{
this.Cursor = old;
return;
}
//otherwise proceed as normal
string curItem = Tab[position];
string df = Flocations[position];
string f = ";Extended Properties=\"Excel 12.0;HDR=NO\"";
string c = "Provider=Microsoft.ACE.OLEDB.12.0;Data Source=" + df + f;
DataSet data = new DataSet();
OleDbConnection con = new OleDbConnection(c);
//var
DataTable dataTable = new DataTable();
string query = string.Format("SELECT * FROM [{0}]", curItem);
OleDbDataAdapter adapter = new OleDbDataAdapter(query, con);
try
{
con.Open();
adapter.Fill(dataTable);
data.Tables.Add(dataTable);
explrdgv1.AutoGenerateColumns = true;
explrdgv1.DataSource = data.Tables[0];
string s = data.Tables[0].TableName;
explrdgv1.AllowUserToAddRows = false;
explrdgv1.MultiSelect = false;
explrdgv1.ReadOnly = true;
//now we populate Tseries
int m = 0, n = 0;
m = data.Tables[0].Rows.Count;
n = dataTable.Columns.Count;
this.R = m;
this.C = n;
int q = 0, r = 0;
try
{
for (q = 0; q < m; q++)
{
for (r = 0; r < n; r++)
{
double d = (double)data.Tables[0].Rows[q].ItemArray[r];
Tseries.Add(d);
}
}
//call the HookUpData method which will assist with the production of charts etc
HookUpData();
}
catch (Exception x)
{
explrForm_hide();
MessageBox.Show("No data in the underlying tab. Error:" + x.Message);
}
}
catch (Exception ex)
{
MessageBox.Show("The listbox selection did not work. Error: " + ex.Message);
}
finally
{
con.Close();
this.Cursor = old;
//save down a CSV copy of the underlying data in case the user wants it
curItem = curItem.Replace("'", "");
curItem = curItem.Replace("$", "");
string p = curItem + ".csv";
string q = df.Replace(".xlsx", p);
StreamWriter sw = new StreamWriter(q, false);
sw.WriteLine("Data");
foreach (double d in Tseries)
{
sw.WriteLine(d);
}
sw.Close();
}
}
