/// <summary>
/// Mengestimasi parameter
/// </summary>
private void EstimateParameters()
{
if (this.sesRdb.Checked == true)
{
if (this.variable.SeriesValuesNoNaN.Count < 7)
{
MessageBox.Show("Unsufficent number of observations", "Exponential Smoothing", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.DialogResult = DialogResult.None;
return;
}
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
this.esProperties.initialModel = 1;
ses = new SingleExponentialSmoothing(this.variable, this.esProperties.alpha, this.singleInitialSmoothed);
this.esTable.smoothed = ses.Smoothed;
this.esTable.predicted = ses.Predicted;
this.esTable.residual = ses.Residual;
this.esProperties.includedObservations = ses.IncludedObservations;
this.esProperties.sseES = ses.SSE;
this.esProperties.mseES = ses.MSE;
this.esProperties.maeES = ses.MAE;
this.esProperties.mpeES = ses.MPE;
this.esProperties.mapeES = ses.MAPE;
}
else if (this.brownRdb.Checked == true)
{
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
this.esProperties.initialModel = 2;
brown = new DoubleExponentialSmoothingBrown(this.variable, this.esProperties.alpha);
this.esTable.smoothed = brown.Smoothed;
this.esTable.predicted = brown.Predicted;
this.esTable.residual = brown.Residual;
this.esProperties.includedObservations = brown.IncludedObservations;
this.esProperties.sseES = brown.SSE;
this.esProperties.mseES = brown.MSE;
this.esProperties.maeES = brown.MAE;
this.esProperties.mpeES = brown.MPE;
this.esProperties.mapeES = brown.MAPE;
}
else if (this.holtRdb.Checked == true)
{
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
//get gamma
try { this.esProperties.gamma = double.Parse(this.gamma.Text); }
catch { this.esProperties.gamma = 0.2; }
this.esProperties.initialModel = 3;
holt = new DoubleExponentialSmoothingHolt(this.variable, this.esProperties.alpha, this.esProperties.gamma);
this.esTable.smoothed = holt.Smoothed;
this.esTable.trend = holt.Trend;
this.esTable.predicted = holt.Predicted;
this.esTable.residual = holt.Residual;
this.esProperties.includedObservations = holt.IncludedObservations;
this.esProperties.sseES = holt.SSE;
this.esProperties.mseES = holt.MSE;
this.esProperties.maeES = holt.MAE;
this.esProperties.mpeES = holt.MPE;
this.esProperties.mapeES = holt.MAPE;
}
else if (this.winterRdb.Checked == true)
{
if (this.variable.SeriesValuesNoNaN.Count < 4)
{
MessageBox.Show("Unsufficent number of observations", "Exponential Smoothing", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.DialogResult = DialogResult.None;
return;
}
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
//get gamma
try { this.esProperties.gamma = double.Parse(this.gamma.Text); }
catch { this.esProperties.gamma = 0.2; }
//get beta
try { this.esProperties.beta = double.Parse(this.beta.Text); }
catch { this.esProperties.beta = 0.2; }
//get seasonal length
try { this.esProperties.seasonalLength = int.Parse(this.seasonalBox.Text); }
catch { this.esProperties.seasonalLength = 2; }
this.esProperties.initialModel = 4;
winter = new TripleExponentialSmoothingWinter(this.variable, this.esProperties.alpha, this.esProperties.gamma, this.esProperties.beta, this.esProperties.seasonalLength, this.esProperties.isMultiplicative);
this.esTable.smoothed = winter.Smoothed;
this.esTable.trend = winter.Trend;
this.esTable.seasonal = winter.Seasonal;
this.esTable.predicted = winter.Predicted;
this.esTable.residual = winter.Residual;
this.esProperties.includedObservations = winter.IncludedObservations;
this.esProperties.sseES = winter.SSE;
this.esProperties.mseES = winter.MSE;
this.esProperties.maeES = winter.MAE;
this.esProperties.mpeES = winter.MPE;
this.esProperties.mapeES = winter.MAPE;
}
}
/// <summary>
/// Mengestimasi parameter
/// </summary>
private void EstimateParameters()
{
if (this.sesRdb.Checked == true)
{
if (this.variable.SeriesValuesNoNaN.Count < 7)
{
MessageBox.Show("Unsufficent number of observations", "Exponential Smoothing", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.DialogResult = DialogResult.None;
return;
}
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
this.esProperties.initialModel = 1;
ses = new SingleExponentialSmoothing(this.variable, this.esProperties.alpha,this.singleInitialSmoothed);
this.esTable.smoothed = ses.Smoothed;
this.esTable.predicted = ses.Predicted;
this.esTable.residual = ses.Residual;
this.esProperties.includedObservations = ses.IncludedObservations;
this.esProperties.sseES = ses.SSE;
this.esProperties.mseES = ses.MSE;
this.esProperties.maeES = ses.MAE;
this.esProperties.mpeES = ses.MPE;
this.esProperties.mapeES = ses.MAPE;
}
else if (this.brownRdb.Checked == true)
{
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
this.esProperties.initialModel = 2;
brown = new DoubleExponentialSmoothingBrown(this.variable, this.esProperties.alpha);
this.esTable.smoothed = brown.Smoothed;
this.esTable.predicted = brown.Predicted;
this.esTable.residual = brown.Residual;
this.esProperties.includedObservations = brown.IncludedObservations;
this.esProperties.sseES = brown.SSE;
this.esProperties.mseES = brown.MSE;
this.esProperties.maeES = brown.MAE;
this.esProperties.mpeES = brown.MPE;
this.esProperties.mapeES = brown.MAPE;
}
else if (this.holtRdb.Checked == true)
{
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
//get gamma
try { this.esProperties.gamma = double.Parse(this.gamma.Text); }
catch { this.esProperties.gamma = 0.2; }
this.esProperties.initialModel = 3;
holt = new DoubleExponentialSmoothingHolt(this.variable, this.esProperties.alpha, this.esProperties.gamma);
this.esTable.smoothed = holt.Smoothed;
this.esTable.trend = holt.Trend;
this.esTable.predicted = holt.Predicted;
this.esTable.residual = holt.Residual;
this.esProperties.includedObservations = holt.IncludedObservations;
this.esProperties.sseES = holt.SSE;
this.esProperties.mseES = holt.MSE;
this.esProperties.maeES = holt.MAE;
this.esProperties.mpeES = holt.MPE;
this.esProperties.mapeES = holt.MAPE;
}
else if (this.winterRdb.Checked == true)
{
if (this.variable.SeriesValuesNoNaN.Count < 4)
{
MessageBox.Show("Unsufficent number of observations", "Exponential Smoothing", MessageBoxButtons.OK, MessageBoxIcon.Error);
this.DialogResult = DialogResult.None;
return;
}
//get alpha
try { this.esProperties.alpha = double.Parse(this.alpha.Text); }
catch { this.esProperties.alpha = 0.2; }
//get gamma
try { this.esProperties.gamma = double.Parse(this.gamma.Text); }
catch { this.esProperties.gamma = 0.2; }
//get beta
try { this.esProperties.beta = double.Parse(this.beta.Text); }
catch { this.esProperties.beta = 0.2; }
//get seasonal length
try { this.esProperties.seasonalLength = int.Parse(this.seasonalBox.Text); }
catch { this.esProperties.seasonalLength = 2; }
this.esProperties.initialModel = 4;
winter = new TripleExponentialSmoothingWinter(this.variable, this.esProperties.alpha, this.esProperties.gamma, this.esProperties.beta, this.esProperties.seasonalLength, this.esProperties.isMultiplicative);
this.esTable.smoothed = winter.Smoothed;
this.esTable.trend = winter.Trend;
this.esTable.seasonal = winter.Seasonal;
this.esTable.predicted = winter.Predicted;
this.esTable.residual = winter.Residual;
this.esProperties.includedObservations = winter.IncludedObservations;
this.esProperties.sseES = winter.SSE;
this.esProperties.mseES = winter.MSE;
this.esProperties.maeES = winter.MAE;
this.esProperties.mpeES = winter.MPE;
this.esProperties.mapeES = winter.MAPE;
}
}
private void GridSearch()
{
double alphaStart = (double)this.alphaStartUpDown.Value;
double alphaStop = (double)this.alphaStopUpDown.Value;
double alphaIncrement = (double)this.alphaIncrementUpDown.Value;
double currentAlpha = alphaStart;
double solutionCount = double.Parse(solutionBox.Text);
double maxSolutionsCriterion = 0;
int maxSolutionIndex = -1;
int increment = 0;
while (currentAlpha <= alphaStop)
{
SingleExponentialSmoothing ses = new SingleExponentialSmoothing(variable, currentAlpha);
double mse = ses.MSE;
// Masukkan sejumlah solutionCount solusi pertama ke list
if (solutions.Count < solutionCount)
{
solutions.Add(ses);
if (ses.MSE > maxSolutionsCriterion)
{
maxSolutionsCriterion = ses.MSE;
maxSolutionIndex = solutions.Count - 1;
}
}
// list solusi sudah penuh
else
{
// jika lebih kecil dari MSE terbesar pada list, ganti solusi terbesar itu
// dengan solusi ini
if (ses.MSE < maxSolutionsCriterion)
{
solutions[maxSolutionIndex] = ses;
maxSolutionIndex = this.FindMaximumCriterionIndex(solutions);
maxSolutionsCriterion = solutions[maxSolutionIndex].MSE;
}
}
currentAlpha += alphaIncrement;
++increment;
}
//this.textBox2.Visible = true;
//this.textBox2.Text = increment.ToString();
// urutkan berdasarkan mse
solutions.Sort(delegate(SingleExponentialSmoothing ses1, SingleExponentialSmoothing ses2) { return ses1.MSE.CompareTo(ses2.MSE); });
this.resultGrid.RowCount = solutions.Count;
// tampilkan ke grid
for (int i = 0; i < solutions.Count; i++)
{
int row = i + 1;
this.resultGrid.Rows[i].HeaderCell.Value = row.ToString();
this.resultGrid[0, i].Value = solutions[i].Alpha.ToString("F3");
this.resultGrid[1, i].Value = solutions[i].MAE.ToString("F5");
this.resultGrid[2, i].Value = solutions[i].MSE.ToString("F5");
this.resultGrid[3, i].Value = solutions[i].MPE.ToString("F5");
this.resultGrid[4, i].Value = solutions[i].MAPE.ToString("F5");
}
}
private void GridSearch()
{
double alphaStart = (double)this.alphaStartUpDown.Value;
double alphaStop = (double)this.alphaStopUpDown.Value;
double alphaIncrement = (double)this.alphaIncrementUpDown.Value;
double currentAlpha = alphaStart;
double solutionCount = double.Parse(solutionBox.Text);
double maxSolutionsCriterion = 0;
int maxSolutionIndex = -1;
int increment = 0;
while (currentAlpha <= alphaStop)
{
SingleExponentialSmoothing ses = new SingleExponentialSmoothing(variable, currentAlpha);
double mse = ses.MSE;
// Masukkan sejumlah solutionCount solusi pertama ke list
if (solutions.Count < solutionCount)
{
solutions.Add(ses);
if (ses.MSE > maxSolutionsCriterion)
{
maxSolutionsCriterion = ses.MSE;
maxSolutionIndex = solutions.Count - 1;
}
}
// list solusi sudah penuh
else
{
// jika lebih kecil dari MSE terbesar pada list, ganti solusi terbesar itu
// dengan solusi ini
if (ses.MSE < maxSolutionsCriterion)
{
solutions[maxSolutionIndex] = ses;
maxSolutionIndex = this.FindMaximumCriterionIndex(solutions);
maxSolutionsCriterion = solutions[maxSolutionIndex].MSE;
}
}
currentAlpha += alphaIncrement;
++increment;
}
//this.textBox2.Visible = true;
//this.textBox2.Text = increment.ToString();
// urutkan berdasarkan mse
solutions.Sort(delegate(SingleExponentialSmoothing ses1, SingleExponentialSmoothing ses2) { return(ses1.MSE.CompareTo(ses2.MSE)); });
this.resultGrid.RowCount = solutions.Count;
// tampilkan ke grid
for (int i = 0; i < solutions.Count; i++)
{
int row = i + 1;
this.resultGrid.Rows[i].HeaderCell.Value = row.ToString();
this.resultGrid[0, i].Value = solutions[i].Alpha.ToString("F3");
this.resultGrid[1, i].Value = solutions[i].MAE.ToString("F5");
this.resultGrid[2, i].Value = solutions[i].MSE.ToString("F5");
this.resultGrid[3, i].Value = solutions[i].MPE.ToString("F5");
this.resultGrid[4, i].Value = solutions[i].MAPE.ToString("F5");
}
}